The Ironhack Blog

Data is everything. And you definitely already know that the best decisions are backed by data, so using data to drive business growth should be an absolute no-brainer. But how can you do that? What does using data for your business even mean?! We’ll explore these questions and much more below. What is Data? This is a great place to start–knowing what data is can help you determine exactly what kinds of data you have in your company, the kinds that could be of value to you, and what to keep an eye out for. Simply put, data is information that has been collected or ordered to serve a particular purpose , usually drawing conclusions. Data can come in all shapes and sizes, but let’s cover six of the main ones : Quantitative data : this type of data is numeric in nature, such as numbers, percentages, and units of time. For studies where results are measurable, quantitative data is very useful. Qualitative data : on the other hand, qualitative data describes the characteristics of a subject in non-numeric formats, such as descriptions, words, or images/videos. Results of a focus group, which are written answers, are an example of qualitative data. Nominal data : this kind of data is useful for grouping information, such as when you’re trying to separate survey participants by sex, age, or other variables. Ordinal data : as the name suggests, ordinal data is used for data that has a specific order to show a certain trend or performance. This order cannot be changed, as the purpose is to use the predetermined order of the data for a specific use, such as determining the winner of a race or the highest SAT score. Discrete data : working uniquely with whole integers, discrete data can’t be separated into parts and instead is used to help businesses place product orders, review worker performance, or determine company growth. Continuous data : this type of data can be broken down into smaller parts and used to determine more specific matters, such as production or delivery time. All of these different types of data come together to form valuable insights that can help businesses make the right decisions. Why is Data Important? We’re glad you asked! Data is absolutely essential to business success because it allows companies to place reasoning behind their decisions and back up their choices with actual facts. And when we refer to “data,” we’re referring to a wide range of sources: statistical information, analytical data, customer insights and feedback, sales number, and practically any other information that a company has. And why is this data so important to businesses? Companies that use data to make their decisions: Benefit from easier decision making : it’s a lot easier to make the right decisions when there’s data backing your decisions. For example, if you’re deciding between two different marketing strategies and past data shows you that one was much more effective than the other, you’ll be able to back up your decision with evidence from the last time a similar strategy was executed. Have higher revenue/profits : apart from the fact that data-driven decisions tend to be more lucrative as they are thought out and well-planned, companies that use data to make their decisions can help companies optimize their product/service offering and predict future sales. Are better at problem solving : trying to solve a problem with little to no information can be quite the challenge. When presented with a problem, companies can use past data to evaluate different options and choose the best possible solution. Personalize the customer experience : data isn’t just about numbers, using customer data to track what they’ve purchased and searched can help you better anticipate what your clients will want in the future and cater their experiences to them specifically. Now that you’re familiar with the different types of data and how it can benefit your business, let’s explore just that: using data to drive business growth. Using Data to Drive Business Growth Ask anyone successful how they’ve reached that point and they’ll all have the same answer: using data to make smart business decisions. And while there are entire careers dedicated to simply analyzing data, let’s explore a few simple and easy ways you can use data to transform your business’ growth. Explain the value of data to your colleagues : using data to make decisions is only valuable if everyone decides to do so. Choosing to be a data-driven company means that all employees are dedicated to making the most of available data and, most importantly, backing up their decisions with strong data. Define KPIs and benchmarks : using data for decisions is great, but ensuring that you have measures in place to evaluate your results over time is also key. And speaking of key, identifying key performance indicators and benchmarks is necessary to check out your growth and help future decisions be even stronger. Learn how to collect and organize data : as you saw above, there are lots of different types of data. And even though you might struggle to filter through the information and see what is actually of use, choosing the right data to study will make all the difference. Decide what’s important to you and your company and then focus on that specific information. Analyze the data properly : data analytics is an entirely separate field; there are professionals who solely analyze data. It’s complicated and requires study and experience, but these are five steps to properly analyzing data: Define your question and goals: why are you analyzing this data? What conclusions do you hope to draw? What do you hope is your end result? Answer these questions and you’ll have a better idea of what you should get out of the data. Collect the data : now that you know what you want to accomplish with your data, start collecting the data based on what you need to have to make your decisions. If your goal is to provide customers with the ideal online experience, then make sure you collect data regarding what they’re searching for, what competitors offer, and customer demographics. Data wrangling : with all the data collected, it’s time to make sure your data doesn’t have outliers, null values, or incorrectly recorded information. It can be quite tedious and boring, but cleaning up your data for analysis is just as important as the analysis itself. Decide your type of analysis : there are lots of ways to analyze data and choosing the right one depends on what you wish to accomplish. Research possible options and choose the best one for your desired solution. Interpret the results : you’ve collected, cleaned, and analyzed the data: it’s time to interpret the results! You may need to visualize the results in graph form or display it in charts; this depends on your specific data set and, of course, your audience. Data is an incredible way to ensure your business decisions are properly made and with just a little bit of time and dedication, you and your team can be well on your way to data-backed decisions that will spur business growth. Sounds like a dream, right?! At Ironhack, our Data Analytics Bootcamp is designed to ensure that you enter the workforce with the proper tools to both identify, collect, and analyze data. Interested? Check it out.

When you think of bootcamps, you might just think of people crowded around computers, working together to learn everything they can in a short period of time. And while yes, lots of bootcamps (especially those in tech!) do resemble this image, there’s actually a whole separate realm of bootcamps: online or remote bootcamps. Already renowned for their flexibility, online bootcamps take said flexibility a step further and eliminate commuting requirements, offering students the chance to take their bootcamp from wherever they prefer. On a high level, this guarantees that: Students can access bootcamps in other parts of the globe, which would be impossible if the class was taught in-person Students can choose the location (or locations) where they want to study; for students that like a change of scenery, this can be extremely beneficial Those who have other responsibilities, such as childcare or family duties, can stay at home and not miss out on classes Many remote bootcamps allow students to watch classes or report work at various times, meaning that they’re accessible for all students, even those that might have to work nights or work other jobs during the day These are just a few reasons why online bootcamps have become so popular and we could go on and on. But you probably have a few questions about the differences between remote and in-person bootcamps--and if you’ll receive the same quality education, including access to the other perks that bootcamps offer, such as career services and networking. Before we cover the details surrounding remote bootcamps and what to expect from one, let’s dive a bit deeper into all the benefits of online bootcamps. Benefits of Online Bootcamps We mentioned a few above, such as flexibility, but we wanted to go a bit more in-depth about why online bootcamps are such great choices for a wide range of profiles. We could go on and on, but we’ve summed it up into six points: Comfort : imagine making a total career change and never even leaving your sofa! The world has become increasingly digital and education is evolving right alongside it; gone are the days of having to get up early, pack lunch, and head to class. Now you can stay in your pajamas and learn, making the experience overall better. Flexibility : are you responsible for children during the daytime? Or maybe work nights, meaning you need to sleep during the day? Or better yet, maybe you have a dog that needs to be walked every few hours and hiring a dog walker would be yet another expense. No matter your reason, online bootcamps let you maintain your responsibilities while kickstarting your career change. Added confidence: entering a brand new field can be quite the challenge and even more so if you’re shy or unsure about speaking up in class. For those who are more comfortable learning on their own or reaching out to teachers in private, remote courses offer a sense of security that in-person ones lack. Preparation for the real world: tech has always been a flexible field, but since the COVID-19 pandemic, the majority of jobs are entirely remote or hybrid, meaning you need to know how to work from home and get your work done. Taking your bootcamp online can be a great way to test the waters and see what works for you. Expanded opportunities : once you decide to take a bootcamp online, you have tons of options available to you; you’re not limited to just ones nearby. This means that entirely new fields have now become a possibility. Keep in mind, however, that some bootcamps are taught live and others via recorded sessions, so depending on your time zone and availability, you might still have some limitations. Global network: all your classmates will be attending remotely, in addition to teachers and staff that come from diverse places. Your network will grow accordingly and introduce you to new aspects of tech that you hadn’t considered beforehand. Remote vs. In-Person Bootcamps Having trouble deciding if remote bootcamps are right for you? We get it; it’s a tough decision. Both are great options; take a look at these bullet points and see if either resonates more with you: Remote bootcamps are right for you if… You’re a bit unsure about starting a new course and want to study more independently, without the fear of being scrutinized or judged by other classmates. You’re confident in your ability to stay focused and on track, reaching out to other students or instructors for help when needed. You need to maintain other responsibilities, such as childcare, pet care, or a part-time job. You can’t attend in person for whatever reason but want to receive top tier instruction anyways. However, in-person bootcamps might be the better choice if… You benefit from stricter timetables and schedules, opting for a more guided learning method. You’re located close to a campus, meaning you’re able to commute without giving up too much free time. You are looking for regular and constant interaction with your fellow students and instructors. You’re able to be present at campus for eight to nine hours a day, without other responsibilities to stress about. Now that we’ve established the guidelines for choosing an online or in-person bootcamp, you might still be a little unsure about the bootcamps themselves and what to expect from an online course. After all, an institution that offers both remote and in-person bootcamps in the same subject can’t expect us to believe that they’re the same, right?! And you’re right--they’re definitely not the same. But there’s no superior option, just the one that works best for you. Let’s explore how remote and in-person classes differ (apart from the obvious, of course!) and cover exactly what online bootcamps are. Online Bootcamps: a breakdown The most important factor to keep in mind with remote bootcamps is this: is it live or asynchronous? Here’s the difference: Live bootcamps are taught on a specific schedule , such as 9am - 5pm. Despite being held virtually, lectures and classes are not recorded and available for later use. These bootcamps will typically have a strict attendance policy, expecting you to be solely dedicated to classes during this time. Important: if you’re looking to take a remote bootcamp because of other obligations during the normal class time, make sure you do not choose a live bootcamp. Think about live bootcamps as exactly the same time and attention requirement of an in-person one, just from the comfort of your own home. Don’t forget to check the timezone of the course if it’s not local; you don’t want to end up stuck to a crazy schedule in a different timezone. Asynchronous bootcamps don’t demand your presence at a certain time and offer one of the following options: the option to attend remote classes live or watch those classes later as a recording and solely recorded classes. Assignments are typically due within windows and different time zones are welcomed. Important: make sure you properly assess if asynchronous bootcamps are right for you beforehand; just because you’re free to make your own schedule doesn’t mean that you can expect a lower time commitment or less work. You’ll have to be on top of responsibilities, both in the bootcamp and outside, to ensure that you are keeping up with everything. How do online bootcamps work? The most important thing that remote bootcamps demand is a strong internet connection; others may require that you have a certain type of computer with a suggested operating system. It seems like an easy thing to have, but remember that if your internet goes down and you can’t access a class, especially during live courses, you’ll be marked as absent and miss classwork. Test your WiFi signal at home and have a backup plan in case something goes wrong: a phone hotspot or a local public connection, for example. You’ll find that remote bootcamps follow the general schedule of other courses. You’ll have a couple hours of lectures, group work, time for questions and, of course, breaks. Through online tools like Zoom or Blackboard, you’ll be able to share your work with others and receive instructor feedback. Well-designed remote bootcamps will ensure you receive the same education as if you were sitting in a classroom with your fellow students, getting face-to-face instruction. But that’s not all: remote bootcamps, especially those that offer in-person options, can go beyond teaching and provide career services, networking opportunities, and even financing options to make your bootcamp more affordable: Career services: here’s a fact: you’re taking a bootcamp to switch jobs completely or make a transition within tech and you want to find a job. We get it and that’s why you should only consider bootcamps that offer robust career services, helping you polish your resume and portfolio, practicing interviews, and putting you in touch with tech companies. Networking opportunities : because bootcamps are held over such a short period of time, you need to take advantage of every chance to meet fellow techies; quality bootcamps offer students the chance to network with alumni and instructors (even in-person if the institution offers classes on campus!). Financing options : bootcamps are quite the investment and institutions that fully recognize those are ones that take your education seriously. Check out what financing options they offer, such as scholarships, income share agreements, government grants, or payment plans. Online bootcamps are a great tool for many and open the door to tech for a large group of people that otherwise wouldn’t be able to take such a course. But they do demand a certain level of independence and determination and certainly aren’t for everyone. If you’ve made it this far, however, we’re sure you’re strongly considering them: we can’t wait to see your application!

When it comes to building websites, security should be the priority of all web designers. After all, if your website isn’t secure, then you can’t guarantee that your and your client information is safe from hackers and those with bad intentions. Before we head into tips for building secure websites and the key points to keep in mind when constructing your web page, let’s lay out some of the defining factors of secure websites and, of course, why it’s essential. Why is Website Security So Important? You might think that hackers are only looking to take down major bank or corporation websites, but the reality is that every single website that collects any form of identifying information is at risk. Don’t believe us? Check out these numbers : 43% of cyberattacks target small businesses Just 14% of small businesses prioritize cybersecurity 60% of small companies go out of business just six months after a cyberattack To put it simply, website security is essential to protect both client and company information. Ensuring that all your data is safe should be enough to convince you that investing in cybersecurity and safe websites is necessary, but here are a few more reasons: Your business’ reputation is at stake–from both word of mouth and SEO : clients want to trust the companies they use and if your company is known for experiencing hacks or data breaches, word will spread and people won’t feel comfortable entering private information, such as payment details. From an SEO perspective, Google penalizes websites without SSL, making them appear lower in search results and even warning customers when they click on your website. The number of hacks is rising rapidly : cybersecurity is advancing, yes, but so are cyberattacks and as the technologies and tools we have to protect our websites and private information expand, cyber criminals are becoming increasingly creative and finding new ways to hack. This means that cybersecurity and website safety as a whole is a constantly evolving task, and not one that’s just done once. Recovering from a cyberattack is expensive : let’s bring money into the equation. In addition to the reputational damage that a cyberattack will bring to your company, you will have to hire outside experts to first remove the bugs or malware and then verify that absolutely every part of your website is clean and hacker-free. This can cost thousands of dollars and lose you valuable time on the market during the days that this clean-up is happening. There are lots of other reasons why creating secure websites is so crucial, such as protecting the longevity of your business and revenue, but we’ll leave you with one jaw-dropping figure: websites are hacked 30,000 to 50,000 times a day. If you don’t want to be part of this statistic, it’s time to prioritize safe websites. What Makes a Website Secure? The aforementioned concerns might seem scary and, well, they are. But thankfully, there are lots of measures you can take to ensure website safety, no matter the size of your business. Secure websites boast the following: Updated systems : all aspects of your website should be constantly updated, such as your server, CMS, software, and any apps or plugins. Why? Well, the vast majority of updates include important safety changes which address new and specific cyberthreats that have recently arisen. Most programs offer the option to turn on automatic updates or be notified when updates are available; if this isn’t an option, make sure to regularly check for updates on your own. Strong login information : you definitely know this already, but weak passwords are a hacker’s dream. Make sure you stay away from simple passwords or generic, company-wide ones, and also advise your customers to create secure and complex passwords. If you struggle to remember long and complex passwords or need various people in the company to have access to the same account, try using secure password sharing programs like 1Password or LastPass. HTTPS status : this stands for Hypertext Transfer Protocol Secure and encrypts the data exchanges between your site and users, ensuring it doesn’t end up with hackers. The “S” at the end of HTTPS (secure) is only granted for websites that have an SSL certificate, which provides additional security. To figure this out quickly, websites that do have this added security guarantee have https at the beginning of their URL; those that don’t just have http. Constant review : as we mentioned above, cyberattacks are evolving just as fast as cybersecurity and it’s on you to ensure that your website is constantly reviewed and updated to fight the newest threat. If you can’t afford an in-house cybersecurity team, try outsourcing to a freelancer or small organization that does this kind of work; there are also online programs that can run an effective scan of your website. No matter what you choose, this safety review should be frequent. Regular backups : part of creating a secure website is preparing for the worst; this means preparing for what you can do in the case of a hack or data breach. If you backup your website and data with a third party hosting company, you’ll be protected in case you suffer an attack and have your site back in action in just a few hours. Tips and Tricks for Building a Secure Website Ready to get started building a secure website? In addition to the identifying factors we listed above, building a secure website is possible with the following steps: Encrypting your data : websites are made of data and while it’s acceptable to leave general data in the open, personal and sensitive data must be encrypted. What does this mean? Encrypting data basically transforms data into another form so that only people with that specific access can see it. This helps add another layer of security to data storage; data encryption storage isn’t too costly and some database systems today even boast built-in encryption options. What kinds of data should be encrypted? Here’s a quick list: User IDs/emails Passwords Social security numbers Date of birth information Payment information Healthcare records Requiring complicated passwords and two-factor authentication : did you know that 63% of confirmed data breaches are due to weak passwords? Establishing password requirements can help both your company and client data stay safe; two-factor authentication helps fight against hacks where a password is guessed or stolen by mandating a second verification from another account/device, therefore protecting the user’s account even though the password was entered correctly. High-quality passwords combine uppercase and lowercase letters, symbols, and numbers; they also aren’t passwords that have been used in the past. Keeping track of user access information : this type of security isn’t related to preventive security measures; on the other hand, using tools like Google Analytics to track suspicious login attempts from random locations or browsers can trigger an alarm and alert the user. Preventing spammers through SPF, DKIM, and DMARC protocols : each of these tackles a specific issue when it comes to protecting your email domain : SPF : The Sender Policy Framework allows you to decide exactly which servers can be used to send emails from your domain, eliminating any risk of a third-party accessing your domain remotely and sending fake emails from your company. DKIM: DomainKeys Identified Mail, similar to SPF, allows you to define who can send emails from the domain. DMARC : Domain-based Message Authentication is a reporting system that alerts you if anyone tries to send emails through your domain. Managing the website properly: part of ensuring overall site security is staying alert and aware of what’s going on. Depending on the size of your company, this might be a job for just a few people, or an entire team. However, on a general level, you should: Make sure you’re up-to-date with exactly who on your team has access to specific login information and limit who can make changes and see sensitive information. Don’t be generous with these permissions and allow access only to those who absolutely need it. Educate your team about proper cybersecurity practices, ensuring that everyone knows what malware and phishing scams look like and what to do in the case of a hack/data breach. It seems like a lot of steps, we know! But building safe websites is both possible and necessary, meaning it’s well worth your time and money to ensure that your information and website is secure. And if you’re interested in taking the next steps to building that secure website, our Web Development Bootcamp is exactly what you need! Or if you want to handle more of the cybersecurity side of things, our Cybersecurity Bootcamp will teach you exactly what you need to know to help companies secure their servers and protect user and company information.

As a web developer, you have lots of options available to you. First, you can choose if you want to work on the front or back end, or you can decide if you want to do both and specialize in fullstack web development. And you can decide to pursue only in-person roles, or work remotely from anywhere. These might seem like two big decisions already, but there’s one more: do you want to work as a freelance web developer or in-house? Just like with absolutely everything in life, there are pros and cons to freelance work, and this article will help you lay them out and make the best possible decision for you. Let’s start by defining some of the responsibilities of web developers so that you’re able to see exactly what you’re dealing with. Responsibilities of Web Developers The web development field is vast and, naturally, the responsibilities for each role also vary. But generally speaking, we can split web development tasks into five categories: creating a website, designing a website, testing a website, maintaining a website, and managing a website. Creating a website : this is where coding comes into play; website developers on the back end work to create the structure and framework of the website, allowing it to interact positively with users and following all local and global regulations when it comes to storing user data. The code also has to be easy to change or debug when problems arise, to avoid starting over from zero. Designing a website : for those working on the front end, creating an effective and responsive product that intuitively shows users what it needs is the main goal. Those working here need to understand user behavior and company goals to ensure they work well together. Testing a website : the job isn’t done once the website is launched! Those in charge of testing the website must try to find any possible errors and fix said bugs and continuously use feedback from both the front and back end to make the website better. Maintaining a website : similarly to the above point, those in charge of website maintenance must ensure everything is working properly and work fast to correct any error, especially in the case of a cybersecurity issue. Managing a website’s data : the data sourced from a website is an incredible source of information so those tasked with managing website data must ensure they’re protecting user data, securely storing information, and making use of the gathered information. All of these aforementioned roles will be carried out on a team, meaning you’ll need to work closely with other professionals. There’s also a lot of coordination in web development and different areas of tech; this is a great point to consider when thinking about your future web development career. Are you looking to work on long-term projects for the same company? Do you want a change of pace every once in a while? Let’s dive right into discussing freelance web development and making the right choice for you. Freelancing: What is it? We’re sure you have a good idea of what freelancing is, but just in case, let’s give you a quick definition. Freelancing is when you work for yourself, looking for typically short-term and specialized projects. As your own boss, you’re responsible for both finding work and setting your rates, meaning you have a lot of flexibility. Benefits of freelancing As a freelancer, you’ll have a ton of things that in a traditional, in-house setting would be impossible. Let’s discuss: Flexibility : the biggest benefit you’ll see when people talk about freelancing is flexibility; that’s because freelancers can make their own hours, working around other responsibilities, such as childcare duties, and complete their work on their own schedule. Complete decision-making : long gone are the days of busy work tasked by your boss that you have no interest in. Instead, you’re able to select the projects that you want to do, turning down projects that you aren’t interested in. Autonomy over wages : you’ll set your own prices and fees, ensuring that you’re paid exactly what you decide is fair. These sound great, right?! They are, but with every advantage comes a few disadvantages to be on the lookout for: Being completely on your own : freelancing can be a lonely world and if you’re coming from working in a busy office, staying home and working on your own can be a tough adjustment. As a company of one, you’ll also be responsible for your finances, reporting the correct taxes, sales, marketing, social media, and anything else that makes up your career. Complete financial responsibility : the amount of money you bring it will depend entirely on the work you seek out and complete. There is no set salary and especially as you start out, you may experience months where you surpass your goals and others where you barely break even. A lack of consistency : unless you’re lucky enough to land set contracts with a few companies, you will most likely be constantly on the hunt for work. Like we mentioned above, this means your workload and income will vary significantly, making planning for the future a bit hard. In addition to the aforementioned disadvantages, you have to remember that working as a freelancer means you don’t have the typical company benefits of a set salary, vacation days, sick days, parental leave, bereavement, 401k matching, and much more. However dreary these points may seem, working as a freelancer web developer is the right choice for some people, we promise! Freelance Web Development Freelancing is a popular choice in web development, with both developers choosing to go out on their own and others who are employed full-time by a company looking to pick up some extra work on the side. Regardless of your reasoning, there are lots of opportunities for freelance and contract-based web development work. Lots of smaller companies, for example, can’t afford to hire entire development teams and might look to outsource certain roles. To start planning your freelance web development career, here’s our three step guide to success. Make a plan This is an obvious first step, right?! Before you can do anything else, you need to have a plan for how your career will go. As it will probably take you some time to generate a client base, maintaining your current role (if you have one) before you fully jump into freelancing might be the best choice. As you plan your future, keep these tips in mind: Plan for the worst : assume that it will take you a few months to get a couple clients and maybe double that to have a steady clientele on which you can rely. Before you start out on your freelancing journey, make sure you have the financial security to survive a few months without any income, and even more time without the salary you’re used to receiving. Plan for other aspects of your life: are you currently receiving healthcare benefits from your company? You might now be on your own; make a list of everything you’ll lose from your company and make sure you have something in place to cover that need. Plan for what you’ll offer: to be successful as a freelancer, you’ll need to not only be skilled at what you do, but also fill a market need. Do you research and make sure that your specific offer is something that the market needs; the last thing you want is to put all your eggs into one basket and enter a saturated market. Start off on the right foot As we mentioned above, getting off to a solid start is crucial, which is why we recommend finishing steps 1 and 2 before quitting your current role: Figure out how you’ll publicize your business: letting people know about your skills and offerings is crucial! Talk to other freelancers, do your research, and start getting word out. Figure out your administrative responsibilities : depending on where you live, you may be required to register with the government as a freelancer or follow distinct tax practices. Also, how will you send invoices to your clients? Do you have templates made for contracts? Getting all of the boring administrative responsibilities out of the way before you start working can help alleviate some of the pressure. Research appropriate prices : you don’t want to offer prices that are so high that no one hires you, but you don’t want to undervalue your abilities and skills. Get started Now that you’re prepared to be a freelancer, it’s time to get to work. As a web developer, there are some specific things to keep in mind: Focus on a smaller area at the beginning : there are a lot of freelance developers out there, so try to find the specific audience you’re trying to reach and focus on them, figuring out their needs and working to meet them. From here, you can slowly start expanding into other areas once you have a few consistent clients. Work to continuously improve : tech is constantly evolving and you need to be up-to-date on the latest skills and technologies that clients will demand. You no longer have a boss telling you to review new skills or paying for courses, so it’s on you to ensure your services are up-to-date and reflect market needs. Stay positive : it can be tough at first! But stay positive, get solace from the fact that you are well-prepared and can financially survive a rough start, and work hard to share your skills with potential clients. For the right person, freelance web development can be a great option. But like with anything, it depends on your personality, drive, and what you’re willing to put into it. If this sounds like a challenge made for you, that’s fantastic! Our Web Development Bootcamp at Ironhack prepares students to enter the web development market as both freelancers and in-house professionals; check out the course today and don’t wait any longer!

People who love data really love data. But if you’re someone who always hated math class as a kid, it can be really hard to understand where they’re coming from. How does a bunch of numbers get anyone’s blood pumping? Spoiler alert: data is the undercurrent of pretty much everything we do online. Every time you check the weather, see a movie or show recommendations, track a run on your smartwatch, or book a hotel for your next vacation, data is behind it all. If you’re here, you’re probably curious about data and its various uses. In this article, we’re going to be looking at the biggest impacts that data has on our world and the #1 reason why you should fall completely in love with it. Data Creates the Perfect User Experience This is one data innovation that you’re probably aware of already, as it’s one that fits seamlessly into our day to day lives. But because it’s so seamless, you may be taking it for granted (that’s actually a sign of truly great innovation–you don’t even notice that it’s happening!). So let’s take a second to really appreciate the magnitude of the impact that data has on our cultural tastes. If you’ve used Spotify/Apple Music to find your new favorite to sing on long road trips, the album that’ll get you through your next heartbreak, and the band that you’ll someday travel halfway across the country to see live in concert, you’ve got data to thank for it. Spotify has invested heavily in training machine learning algorithms to personalize the experience for its 406 million global users. That’s a huge impact. But this isn’t just limited to music. Streaming services use huge amounts of customer data to test, personalize experiences, and make big business decisions. Netflix is particularly famous for this, running constant A/B tests to provide users with the best possible experience–and to keep us all binging and chilling. Let’s say you love Ryan Reynolds and you’ve watched a lot of his movies. Netflix knows that little nugget of data about you and will make sure that any movie with Ryan Reynolds features him prominently in the preview, thus encouraging you to watch. Companies like Netflix, Amazon, Disney+, and HBO don’t just use data to build their platform’s user experience. They take the usage data from their streaming services to make important production decisions, looking at things like content abandonment rates, keyword searches, and even which scenes were rewound and rewatched. This information is then used to influence which shows get made or canceled. With the shows created by these streaming/production companies sitting at the forefront of the cultural zeitgeist, big data truly is a global tastemaker! Data is Saving the Planet Data doesn’t just tell Netflix whether to bring Squid Games back for Season 2. It’s also being used in new ways in sustainability and wildlife conservation: GPS tagging and camera traps are able to collect real-time information on the movements of some of the world’s most critically endangered animals, empowering conservation groups with the information needed to potentially save the species. Data scientists are able to track migration, population growth/decline, and identify risks that may threaten an endangered species. Researchers at the Max Planck Institute of Animal Behavior uncovered a problem in the early 2000s whilst attempting to track animal movements in Panama. The thick canopy of the trees could not be penetrated by GPS signal, so they set up a series of radio towers that would allow them to collect the data they needed, but soon found that the sheer amount of information received would overwhelm their current systems. They created Movebank , a software that could safely store and manage real-time global animal movement data. Today, researchers around the world rely on Movebank to provide answers to big-picture questions in conservation science, with over 3.2 billion animal locations leading to almost 7,000 research studies. From monitoring the speed at which polar ice caps melt to the areas most at-risk from illegal poachers, data helps us to understand the state of our world and may provide the insights that help us to save it. Data Helps Save Lives Ever since the world went digital, big data has been used by the medical industry to improve research and patient outcomes. But it has historically been expensive, slow, and inaccessible for non-data experts. Data impacts the medical field in three different ways: Medical research: data is used to help researchers in labs, leading to breakthroughs in how we understand and treat diseases. Hospital operations : hospitals use data much like any other businesses do to monitor staffing, supplies, waiting room time, and insurance claim data. Patient outcomes : data can be used to improve the treatment of individual patients, as well as answering big-picture questions. One of the biggest game changers in recent years for data in medicine was the boom of wearable tech. Patients are able to have their heart rate constantly monitored in a way that doesn't feel annoying or intrusive and medical providers are able to collect a huge amount of data on their day-to-day heart health. By collecting huge amounts of data and gaining a holistic view of a patient’s (or group of patient’s) health, medical professionals are able to give recommendations for preventive care and stop diseases. For example, a doctor who notices that many patients who have sedentary jobs and concerning VO2 max levels can recommend introducing more cardio into their exercise routines. However, this kind of information is highly sensitive and healthcare institutions are rapidly understanding the need for heightened data security. In-house data solutions must be bolstered by top-notch security protocols, including two-factor authentication and routine audits. The ethics of how this data is stored and shared is also a hot topic that the industry constantly grapples with, leaving a need for data experts to guide this ongoing conversation. Data Runs the Business World The old saying goes money makes the world go round . While there’s certainly still plenty of truth to that, what really makes the world go round (namely the business world!) is data . There simply aren’t enough hours in the day to list all of the ways that companies in every corner of the world rely on data and each company has their own unique data sets. But we can of course make a few sweeping generalizations: Customer demographics inform marketing and advertising decisions Product usage data informs UI decisions and helps teams build better digital experiences Big data helps Big Tech launch new features, improve existing products, and launch entirely new innovations Data helps entrepreneurs identify problems to solve and needs to fill Because the business world loves cliches, we’ve got another for you. “Without data, you’re just another person with an opinion,” said W. Edwards Deming in 1982, and it’s still true today. As a professional in any industry, you need data to make the right decisions and get people to back your ideas. It’s the closest thing to the objective truth as we can get and you’ll need it on your side, no matter which capacity you’re working in. Let’s say you’re a UI designer and you’re fighting with an important stakeholder over how big the logo needs to be on the first fold of your homepage. If that stakeholder is the CEO, their authority trumps yours. So you bring out the big guns…results of an A/B test which shows that your version of the homepage design works better within the overall user journey. Alternatively, let’s say you’re in advertising. You and your team have been asked to run a summer campaign for an important client and no one can quite agree on which direction to go in. You need a quick win to turn around a slow quarter, so you go back and look at which previous campaigns knocked it out of the park. You use the data to design a fantastic campaign and convince the rest of the team that it’s the right way to go. While data has often been attributed to the tech industry and giant corporations, there’s actually no limits to who data can help. Data Analytics is an Exciting and Stable Career Path Exciting and stable aren’t usually two words that go together but they’re absolutely the right ones to describe a career in data. A career in data is exciting because data is being used in new ways every da y; the smallest pockets of information are capable of unleashing insights that can change the world. Companies can use data to impact the lives of millions, from saving rare species from extinction to creating breakthroughs in medical science. A career in data is also stable, because data isn’t going anywhere anytime soon . Every single company, even those outside of the tech industry, rely on data every day and there’s no shortage of need for professionals who know how to manage and analyze it. Data is also global, not restricted to any one corner of the world. No matter where you are, there will be a need, a use, and a demand for data. Even if you’re not looking to work specifically as a Data Scientist or Data Analyst, chances are data can be helpful in your day to day work. And maybe the data sets you use won’t change the world on their own, but you’ll be future-proofing your skill set and opening up brand new career doors. You might not change the world, but you can change your world. If you’re interested in diving into the data world, we’d be happy to have you at Ironhack! Our Data Analytics Bootcamp is designed to teach you what you need to know to enter the workforce as a data professional. Check out the course information today and let us know if you have any questions–we can’t wait to see your application!

The expansion of cybersecurity, in both its good and bad forms, is inevitable. People are putting more and more of their lives, be it on social media or the cloud, and cybersecurity plays an important role in ensuring that people’s privacy is protected, no matter what website they use. To understand the important role cybersecurity plays online, we first need to understand the different kinds of cyber threats: Malware is malicious software viruses, disguised as legitimate software, that use ransomware or Spyware to block network access or obtain private information. Another common form of cyberattacks is phishing , where a hacker pretends to be someone else, usually a person of trust, to lure them into providing access to their personal information. Password hacking means that hackers have full access to your accounts, especially if you use the same password for various accounts. There are so many forms of cyberattacks; we’d have to dedicate many posts to covering them all! But today we’re focusing on the future of cybersecurity and what we can expect from upcoming years. You might be thinking well, cybersecurity will just continue advancing, right?! And while yes, that’s what we anticipate, cyber threats will also grow, finding ways to hack the newer systems or coming up with more effective and believable phishing attacks. On a very general level, we can prioritize the future safety of cybersecurity through investing in technology that can protect private data and taking the time to educate staff and stakeholders about the dangers of cyber threats, ensuring they’re taking the right precautions. As we dive into the future of cybersecurity, we’ll explore upcoming trends, the new threats that could arise, and how to combat them. The Future of Cybersecurity Artificial intelligence is probably the biggest trend to look out for and we’re sure you know why. In early 2023, AI tools like ChatGPT took the world by storm, imitating human dialogue and responses in a never before seen way. Artificial intelligence isn’t limited to just chatbots, however; it can automate tasks, study data, and make decisions for humans in just a few moments . As it evolves and improves, however, cybersecurity professionals think that AI will be able to detect potential threats and flag problems faster than humans can realize, greatly helping overall security measures. Another promising aspect of artificial intelligence is its developing ability to learn from past experiences, processing successful cyberattacks and taking measures to prevent them from happening again. We would be amiss, however, to not discuss the challenges and risks that come from artificial intelligence advancements in cybersecurity: Cost : artificial intelligence cybersecurity technology is quite new and while many big companies have the resources to develop AI technologies, the vast majority of smaller companies can’t dedicate the same amount of money. The increased use of artificial intelligence in cybersecurity will be gradual. Advances on the criminal side as well: spoiler alert! Criminals can use artificial intelligence too! The rise of chatbots that are able to mimic the human voice means that more believable phishing attacks will enter the game. Mistakes : artificial intelligence isn’t perfect and some cyberattacks, especially new ones, will slip by. It’s important to not rely too heavily on artificial intelligence and ensure that new technologies are closely monitored. Advances in machine learning will also bring about similar concerns: how will we be able to distinguish between humans and machines? In addition to worries about data protection and cyberthreats, tools like ChatGPT and Google’s Bard have also been proven to provide misinformation every once in a while. Will we reach a place where we don’t trust anything online? As more and more information gets put online, we’ll have to both protect existing data and ensure the veracity and integrity of online information is maintained. Another up and coming theme will be the idea of having and being able to access universal data and information almost instantly . Thanks to developments in the cloud, for example, companies can store large amounts of data through customizable plans that permit them to choose exactly what works for them.  While this sure is convenient and sectors like health care will benefit from the ability to share patient data amongst different professionals and reference past cases for better diagnosis, it also poses quite the security threat. Companies are constantly collecting data from users, such as their identifying information, preferences, account information, banking details, and much more. It’s so common these days that most people don’t even realize it, but the amount of information on the cloud is growing exponentially; a potential security breach or data leak could have catastrophic consequences. To keep up with the need to collect, use, and protect so much information, many companies outsource their IT department and security teams to third-party organizations. Sharing data with another company is a security risk in and of itself and until all companies are truly prepared to handle such large quantities of data, the risk of data breaches will only increase. A Future Career in Cybersecurity As a relatively new field, cybersecurity has been on the rise and will continue to be so. Companies are constantly looking for professionals that know how to predict, handle, and solve cyberthreats, meaning that a career in cybersecurity is a great choice. Here’s what we know: Named the only future-proofed career by the Bureau of Labor Statistics , cybersecurity jobs will continue to grow exponentially over the next few years. Due to the online nature of the role, cybersecurity professionals can work remotely and usually have flexible schedules , making it an attractive option for many. They’re in high demand and in tech: the perfect combination! You can expect high entry-level salaries with lots of room for advancement and growth. According to Garner , companies will spend 11% more in 2023 than they did in 2022 to improve security and risk management. Convinced that cybersecurity is the right path for you? We’re not surprised! With so much growth ahead, studying cybersecurity now will kickstart your tech career and propel you into the workforce. We can’t wait to see you in class!

You’ve heard about big data; after all, it would be nearly impossible to not know what it is today! Big data is all the rage, propelling progress forward and allowing us to reach unforeseen progress in the field of technology. But what exactly is big data? What does it do? Is all that we’ve heard really true? We’ll tackle these topics and much more in this post. What is Big Data? Let’s explain big data: on a very high level, big data refers to the tools and technologies developed to manage large quantities of data. Made up of structured, semistructured, and unstructured data, big data is used in machine learning and data analytics projects to process, interpret, and draw conclusions from these large amounts of data. There’s no specific quantity of data that big data demands; instead, any sort of data that simply cannot be processed by humans is grouped into this category. The three different types of big data are: Unstructured data : this type of data is not ordered or categorized, therefore making it harder to interpret by machine models. Examples include video and audio files, dates, and satellite imagery. Structured data : this data is pre ordered and structured, facilitating the sorting process. Because it's already organized, drawing conclusions from the data and interpreting results is much easier. Examples include transactions and financial records. Semistructured data : semistructured data is just what it sounds like--it’s not completely unorganized like unstructured data, but still lacks the clear parameters of structured data. Examples include streaming data and web server logs. Examples of big data Curious about how big data manifests in the real world? Check out some of these examples of big data in six major sectors: Marketing : major companies like Amazon receive astronomical amounts of data every single minute and through innovative data storing and processing, they can see what clients are searching for and suggest similar objects, or sell the same product again, delivering an ultra-personalized customer experience Healthcare : long gone are the days of sifting through case studies to find similar symptoms or treatments; doctors and medical professionals can now use big data to access previous patient records in just moments, allowing them to quickly and efficiently find a solution. Transportation : has your phone ever suddenly changed your route, alerting you of traffic or a car accident ahead? Using massive amounts of real-time data stored on databases, big data can help you get to your location as quickly as possible. Government : even at a local level, elected officials have tons of constituents and things to keep in mind; with big data databases that are dedicated uniquely to organizing and making sense of local finances and crisis information, government officials are better able to understand the entire picture. Cybersecurity : big data has advanced to a point where we can program the system to detect peculiarities; this is very helpful for detecting possible cyberattacks and unusual behavior. Business : companies have a wide variety of things to keep track of: operations, people and HR, financials, and sales are just a few. So when big data can step in and organize that data and bring conclusions to leadership, it greatly lightens the workload and allows leaders to make data-driven decisions. Why is big data important? You know that making data-driven decisions is an absolute necessity when it comes to business. As companies receive more and more data from even more sources, being able to actually put that data into practice and make better decisions from it can mean improved productivity, higher revenue, better customer services, and overall better company performance. Here’s an example: big data can track and store client experiences on their website, looking at how long they spend on a certain page, the products they search for and buy, and what promotional tools are bringing customers to their site. But for big and small companies alike, manually processing this data in real-time is impossible; there’s simply too much. With big data, however, companies can receive updated and accurate assessments, which can help them guide their future decisions. The five V’s of big data Big data boasts five advantages, frequently referred to as the five v’s: volume, variety, velocity, value, and veracity: Volume : big data can handle just that--lots of data, much more than your typical device or computer, making it extremely valuable. Variety : big data is capable of receiving and organizing a wide range of types of data, even if the sources are quite varied. Velocity : big data has to be able to quickly and effectively receive, store, and process real-time data. Value : these large quantities of data have to be valuable for companies, meaning it must be properly evaluated and stored. Veracity : authenticating the value of the data and its reliability is a crucial part of depending so heavily on big data. Now that you’re clear on what big data is, how it’s important, and what it brings to the table, let’s explore some of the biggest myths and misconceptions surrounding it. Myths and Misconceptions About Big Data You’ve probably heard a lot about big data and it can be hard to sift between what’s real or fake. Well, we’re here to clear up a few of them: Big data is too big Lots of people doubt that big data can actually handle the amount of data it receives! And while we get that concern, remember that tools and techniques have been developed alongside big data to automate the processing steps and experts have devoted their entire careers to understanding big data. If you’re still not convinced, try speaking to an expert so that they can show you how artificial intelligence and other tools help decipher this data effectively. Big data is too expensive for small companies False! Big data solutions, such as the cloud, are actually quite cost-effective, especially when you compare the cost of having and maintaining giant storage installations. Options on the cloud are also entirely customizable, meaning you can decide exactly how much space you need and even test out different options. No matter the size of your company, there are cost-effective solutions for the cloud. Big data is, well, big Here’s a tricky one. We tend to think of big data as a large and overwhelmingly massive quantity of data, right? But actually, big data is made up of lots of little data points, such as an individual transaction or online search. These little data points come together to form big data, which then works to make sense of and find correlation between these points. Big data will replace humans We just know you’ve heard this one! And while big data is capable of storing and processing much more information than humans are capable of, no machine will ever (or at least in the relatively near future!) be able to imitate human decision-making, intelligence, and emotions. The combination of big data working to order and sort the data and then humans using their knowledge and creativity is ideal. Big data will always make the best decisions Just like with any tool in tech, big data can make mistakes; it also lacks the human decision-making capabilities that take various factors, such as environmental and emotional ones, into account. Big data is not flawless, but together with human intelligence, we can harness the power of both parties. Big data is just for tech companies Wrong again! Although it’s typically referred to when talking about tech and data itself, all companies, regardless of sector or size, can benefit from big data to sort through their sales or financial data, to name just a few areas, and make improved, overall business decisions. Big data can predict the future We wish this were true, but it’s not! Just like in any area, the outcome of any proposed solution will depend on hundreds of factors and even if you use predictive models to make your best estimate of what can help, erratic human behavior or force majeure could mean that the prediction is totally off. Conclusions drawn with the help of big data should be carefully reviewed with discretion before setting anything into motion. Big data has taken over! While lots of companies and media outlets are constantly referring to big data and its usage is on the rise, it’s not widely adopted--yet. It’s a relatively new technology and one that requires lots of knowledge; as companies learn more and more about big data and how to harness it for their specific use, we’ll see an increase in use. But for now, the majority of companies are in the first stages of implementing the tool. Did we help bust any myths or misconceptions surrounding data?! We hope so! When used correctly, big data can be a powerful tool that can help your company reach new heights. If you’re ready to join the party and become a big data expert, what are you waiting for?! Check out Ironhack’s bootcamps today.

So you’ve decided that web development is for you and, more specifically, that you want to explore a future in tech as a front end web developer. You’ve heard about JavaScript and know it’s a crucial part of the front end, but aren’t totally sure about what you’ll need to learn. Or have some experience with JavaScript, but want to dive deeper into the client side of web development. It’s also possible you’re torn between the front and back ends, and have decided to be a full stack developer. Just in case you’re a bit confused, here’s the difference: front end developers focus on the client side of web development and full stack developers work on both the client and software side of the website. There’s no right or wrong choice here; it’s up to you and your preferences. But whatever you choose, JavaScript will be an absolute key part of your work. So let’s break it down: what is JavaScript? How does it work? What role does it play on the front end? We’ll answer these questions and give you some tips and tricks to both learning and working with JavaScript throughout this article. What is JavaScript? JavaScript is a programming language used for web development, expanding the capabilities of HTML and CSS to include new and dynamic additions. Thanks to JavaScript, websites can be more dynamic and users can interact with different features. Here’s how: HTML is the structure of the web page and its content CSS offers styles for the content of the web page JavaScript allows you to add a specific style, link to external content, include images, and personalize the user experience. As one of the world’s most popular coding languages, practically every developer you meet will have at least some experience with JavaScript. Why? Well: JavaScript is fast: no one wants to be continuously delayed by slow loading speeds or server connections; because it’s an interpreted language that runs on the client side, it requires less time to secure a server connection, making the program faster. JavaScript runs on the client side: because JavaScript runs on the client side, the server is free from this added weight and can handle other tasks. JavaScript is easy to use: as one of the most widely-known programming languages worldwide, JavaScript is easier to learn because you’ll have a lot of online or peer help if you run into any issues. JavaScript is versatile: JavaScript can be used on both the front and back ends and can be integrated with other tools like Node.js to finish the website. JavaScript is widespread: because the vast majority of developers, on both the front and back end, use JavaScript, it’s widely accepted by browsers and compatible with lots of other languages. JavaScript has lots of features: despite its simple learning curve and widespread knowledge of it, JavaScript boasts lots of added features to enhance your web development project. However, just like anything else, JavaScript does it have its limitations. Let’s take a look: JavaScript can be hacked: because anyone can see the JavaScript script of a website, it could be misused. And because JavaScript knowledge is so widespread, someone could change the code of the website and put the security of client data in danger. JavaScript is harder to debug: compared to other programming languages, it’s harder to see bugs on JavaScript and determine the source. JavaScript is seen differently on each browser: because JavaScript can run on multiple browsers, it must be tested on all to make sure it’s presenting similarly. JavaScript reflects coding errors: one mistake in the code can bring down an entire website, interrupting business functions. Client vs. server side with JavaScript It’s pretty clear, but here’s a quick breakdown: client-side is the part of the websites that clients see and use; the server side is the other side where the foundation of the website is stored. Having a hard time distinguishing between the two? Well, think about when you enter password information into a website and the page loads. The client side is what you just saw and where you entered your information; the server takes that information, validates it, and sends you to the next page. JavaScript can be used on both the client (front end) or server (back end), which is why it’s so commonly used. On the client side, JavaScript’s dynamic nature helps create visually appealing websites that feature images, graphics, different colors, and other marketing tools used to grab your attention. JavaScript Frameworks First, let’s define a framework in web development: just like it sounds, frameworks are a structure that help web developers see what they can build or create. They facilitate the coding process across a wide range of programming languages; the actual programming language is just the characters that make up the code itself. Lots of frameworks are open source, which means that they were designed by a group of software developers and are now available to the public, can be changed or modified, and is freely available for use by anyone. Okay, now that you have that all cleared up, let’s explore some of the best front end (client-side) JavaScript frameworks that can help you harness all its power. Angular Angular is a popular choice for scalable web apps, is free, and supports two-way data binding, allowing your apps to share data amongst themselves: It boasts great templates, letting you achieve high-quality UI designs in little to no time You can create apps easily, mobile or desktop It allows for route-level code splitting, speeding up your app’s speed React This open source framework boasts a huge network of users; it’s easily integrated with any app and was designed by Facebook: It’s easy to debug code with React It offers compatibility across various browsers and boasts a declarative style, facilitating the UI process Using Flux, it supports one-way data binding Vue.js Another popular framework is Vue.js, a fast and simple to use framework: Vue.js offers data binding, which facilitates the assignment of values to HTML It also uses virtual DOM, which makes its performance faster It's easy to learn and offers lots of transition elements Node.js Perfect for web, desktop, and mobile apps, Node.js is a popular choice for server side rendering, but is used for the client side as well: It’s open source and has an incredible community, full of web development professionals giving their advice and helping you out; the online community has also worked together to build new Node.js skills It doesn’t buffer data and offers a low processing time for audio and visual files It’s built on Google Chrome’s V8 JS engine and executes code rapidly Meteor Another open source framework, Meteor boasts one of the best front-end frameworks, Blaze.js: It’s a great fullstack framework for both creating and deploying apps It supports almost all databases that use Node.js It lets you use the same code across the front end, back end, mobile, and desktop apps, making the development process much easier Ultimately, JavaScript’s versatility means that there’s a ton of frameworks out there. Assess your needs, do your research, and then pick the one that best suits your project. Learning JavaScript Now that you’ve seen how great JavaScript is and how it’s one of the best programming languages to add to your resume, it’s time to get to work learning it! Here are a few of our tips and tricks to learning this popular and growing language: Take a bootcamp : if you know that your future lies with coding, a bootcamp is a great way to receive the education you need to reach success in a short period of time and with a much smaller financial investment than if you decided to pursue a college degree. Bootcamps offer flexibility, financing options, career services, and remote courses--no matter your situation, you’ll be able to find the perfect fit for you. Read JavaScript books : if you learn best from reading and already have some initial knowledge of JavaScript, checking out some books may be your best option. Programming language books are typically well edited and reviewed, making sure they provide accurate information (which isn’t always the case online!). Start practicing: whether it’s at a local hackathon or just online by yourself, practice makes perfect and as you get more and more experience with coding, you’ll find yourself moving faster and developing more detailed programs. At the end of the day, it’s up to you, but if you’re truly set on making JavaScript the next skill you add to your resume, then it’s time to dive right in. Check out Ironhack’s Web Development Bootcamp and get the instruction you need to enter the workforce as a JavaScript expert. Ready? We can’t wait to see your application!

So you’ve made a big decision: deciding to start a bootcamp! But now that this decision is out of the way, there’s another one looming: which bootcamp should you choose? There are lots of bootcamps out there that offer a wide range of options: in-person, remote, hybrid, part-time, or full-time. Which is best for you? And what are other factors to keep in mind? We’ll dive into these and much more in this article. The Basics of Bootcamps Before we start choosing bootcamps, we should probably understand what bootcamps are, right?! They’re quite different from traditional learning methods , such as university degrees, but are quite valuable, especially in tech. Why? Well: Bootcamps provide you with a high-quality and specialized education in a specific area, teaching you lots over a short span of time and preparing you to enter the workforce immediately after graduation. Many bootcamps boast extensive flexibility , letting you continue to work or study full-time or maintain other commitments while taking a part-time course. They are significantly cheaper than a four-year degree or year-long certification course but still prepare you to enter the workforce. Since they take place every few months, bootcamp instructors are constantly updating and reviewing their syllabus to ensure they’re teaching the latest market trends. There’s a lot of advantages to bootcamps, as you can see. And while the vast majority have everything we described above, the actual details of each institution will vary significantly and it’s crucial to carefully determine exactly what matters most to you and find the right fit. After all, taking a bootcamp and making a career change is a major choice and you owe it to yourself to make this decision carefully. Choosing the Best Bootcamp The word best is subjective, of course, and ultimately the most important thing is that you’re content with your choice. But when you begin the search, you may be overwhelmed by the pure number of options that exist. We get it and here are our tips and tricks to making sure you’re looking at the right things. Bootcamp topics As we mentioned above, bootcamps are effective because they focus on an incredibly specific topic. Be wary of courses that say they can teach various topics in a short period; your knowledge will be surface level and not help you land a job. Just like with most things in life, it’s best to focus on one thing at a time, become proficient, and then tackle another area. Lastly, make sure your chosen subject aligns with both your professional goals and your interests. For example, if you’re set on working as a data analyst, make sure you pick a program that specifically prepares students to enter the workforce in data analytics, and not one that is more focused on the coding side of tech. Bootcamp costs For most people, this is the deciding factor when it comes to choosing a bootcamp. The financial commitment of taking a bootcamp is enormous and shouldn’t be taken lightly. Make sure you know what you can realistically afford, taking into account your current responsibilities and financial situation. If you would have to quit your current role or pay for childcare to attend a bootcamp, consider that when looking at course costs. Bootcamps are incredibly intensive and require a serious time commitment; if you have to take on a second job during the middle of the course because you can’t make ends meet, you will struggle. Lots of programs, however, offer financial services to help you with the cost of the bootcamp. Look into income share agreements, scholarships, grants, government assistance, or payment plans to see what is realistically an option for you. Bootcamp career assistance You’re probably interested in your chosen topic, but you’re really doing the bootcamp for one reason: getting a job in that field! When researching bootcamp options, think about these questions: Does the institution offer career services? If so, what can I expect? Is it generalized or personalized? Does this help continue after graduation? Do they publicize the job placements of alumni or is there little to no information about success stories? Does the institution help you prepare for the job search? Depending on your situation, these are all crucial things to keep in mind. If you’re coming from years and years in the workforce, your CV is probably up to par, but you might need to polish those interview skills. Think about your specific situation, what matters to you, and what you need and don’t need from your program. Bootcamp level of difficulty The vast majority of students choose bootcamps because they want to dive into a new career and are starting from scratch. But no matter if you have absolutely no previous knowledge on the topic or are pretty well-versed on it, make sure you check out the entrance expectations. Some coding bootcamps, for example, will require you to have some programming language knowledge before starting the course. Others specifically note that no experience is required and include the basics in their curriculum. You don’t want to make a significant impact on your education and show up day one to be thrown into something you’re not ready for or, on the other hand, be taught things you already know. When checking out courses, ask the admissions team for the typical knowledge that students have had for past bootcamps and that should give you an idea of where you stand. Bootcamp content Similarly to checking out the difficulty level beforehand, make sure you ask for a copy of the bootcamp syllabus to ensure you know exactly what will be taught. Some branches of tech can be quite specific and if you’re set on focusing on one area and it turns out that isn’t taught, you’ll be disappointed. For example, if you want to learn Java and the course only teaches Python and CSS, it’s not worth your time nor money. This is also a good opportunity to check in with yourself and be realistic about your own abilities and how you learn. If you learn better from hands-on projects and assignments but the course you’re looking at is more lecture-based, it may not be right for you. The syllabus can also give you a better idea of the amount of information taught and the speed at which it moves, helping you determine if it’s the right fit. Bootcamp students Lots of job opportunities are passed through word of mouth and tech roles are no exception. What’s the typical profile of an incoming student at the institution? What about their background and career goals? Are there collaborative projects or is the course quite independent? These might seem like minor points, but at the end of the day, the answers to these questions could be the difference between you landing a job post-graduation or not. This is a good time to check out the environment and vibe of the institution as well. Does the campus offer networking events with other students or companies or speaker events where you can learn directly from professionals? And if you’re taking a remote course, is there still a sense of camaraderie between students that you can take advantage of when you start advancing in your career? We know that was a lot of information, but this is a big decision! There are tons of bootcamps out there and choosing a certain bootcamp could be exactly what you need to land your dream role. In addition to what we just mentioned, it’s important to keep these in mind as well: Time commitment : if you choose a full-time course, are you able to dedicate the majority of your time to classes and studying outside of class? Do you have any financial obligations that require you to maintain your full-time job? Do you have lots of weddings coming up, meaning you won’t be able to study on weekends? It might seem minor, but your bootcamp will be tiring enough: don’t add more stress on top of it! Location : everything is online these days, but do you learn better in person? Or is the closest bootcamp an hour drive away, meaning your time commitment will be even higher? Carefully consider in-person vs. remote options, being realistic with how you learn and if you will benefit from an instructor and classmates right next to you. Motives for doing a bootcamp: why are you signing up for a bootcamp in the first place?! Ensuring your reasons are clear will help maintain your motivation in tough moments and validate any sacrifices you’ll make as you get started in your new career. Choosing the best possible bootcamp depends on you and your circumstances. Reflect on the aforementioned points, have clear answers to our questions, and then search around until you find the perfect fit. And if you know that Ironhack is ideal for you (we think so!), we’d love to see your application soon.

In the realm of tech, the roles of UX/UI design and data analytics stand apart, each possessing a distinct focus. Data analysts delve into the depths of information to gain valuable insights, while UX/UI designers work to craft attractive interfaces and seamless user experiences. While seemingly different, these disciplines can converge to deliver expectational value . Let’s explore each role and uncover the potential for collaboration between analytics and aesthetics. What is Data Analytics? Data analytics is the examination of vast data sets to uncover meaningful patterns and trends . Embracing data analytics revolutionizes the landscape for tech companies, providing them with the insights that can keep them at the forefront of the industry. Let’s look at some specific ways that data analytics benefits companies: Enhances decision-making: by providing valuable insights based on relevant information, businesses can make informed decisions around everything from marketing to product development. Improves operational efficiency: by analyzing internal data, organizations can identify areas for improvement, streamline processes, and increase productivity. Identifies and addresses risks: by utilizing exploratory data analytics (EDA), analysts can check data holistically, working to detect anomalies, patterns that indicate fraud, and performance issues. Provides competitive advantage: by inspecting market trends, competitor performance, and customer feedback, businesses can continually realign their strategies, giving them a competitive edge. Facilitates targeted engagement: by examining customer data, businesses can identify patterns that indicate customer churn, allowing them to take measures to retain customers and build long-term brand loyalty. The role of a data analyst Data analysts are the minds behind these important discoveries. Utilizing data interpretation and visualization tools, they help organizations unlock the potential of their data . They are responsible for transforming unstructured data into the actionable insights that drive decisions. While specific responsibilities may vary between businesses, the core tasks of data analysts typically include: Data acquisition: analysts collect and organize data from various sources, such as databases, APIs, and spreadsheets. They may use a tool like SQL (Structured Query Language) to extract data from databases and ensure its accuracy. Data verification: analysts ensure that the data is accurate, complete, and free from errors. This may involve removing duplicate entries, handling missing values, and resolving inconsistencies. They use data analytics tools like Excel, Python, or R to perform these tasks. Data interpretation: analysts utilize statistical methods, algorithms, and visualization techniques to dissect data and identify significant patterns. They may apply techniques such as regression analysis, clustering, or time series analysis in order to draw conclusions. Data reporting: analysts transform their findings into visually appealing and accessible reports, charts, and graphs. Employing tools like Tableau, Power BI, or Python libraries like Matplotlib, they create visualizations to help others comprehend their conclusions. Data privacy: analysts maintain the security of the data they work with, ensuring that sensitive information is handled in compliance with the regulations of their organization. They may implement data anonymization techniques, access controls, and data encryption methods. Data presentation: analysts play a crucial role in providing actionable findings and recommendations to their organization. They effectively communicate complex data concepts and insights to non-technical stakeholders, ensuring that conclusions are understood. What is UX/UI Design? User Experience (UX) design and User Interface (UI) design are intertwined disciplines focused on enhancing the usability, accessibility, and satisfaction of digital products . UX design involves crafting the overall user journey; it ensures that users can effortlessly navigate through interfaces. On the other hand, UI design focuses on the visual and interactive elements of an interface, encompassing layout, colors, typography, and interactive components. Let’s look at the value that UX/UI designers bring to organizations: Enhances user satisfaction: by focusing on the needs and preferences of users, UX/UI designers create products that promote a positive user experience. This leads to higher retention rates and increased time spent on platforms. Improves conversion rates: by optimizing the user journey, reducing friction points, and guiding users towards desired actions, UX/UI designers increase sign-ups, purchases, and subscriptions. Provides competitive advantage: by investing in an exceptional user experience, companies can gain an advantage by attracting and retaining more customers. A well-designed and user-friendly interface can distinguish a brand as an industry leader. Promotes cost-savings: by conducting user research, prototyping, and usability testing early in the design process, UX/UI designers address roadblocks early on. This proactive approach saves time, avoids costly design changes, and diminishes potential set-backs. The role of a UX/UI designer Now that we’ve covered the benefits of stellar UX/UI design services, let’s look more closely at the role and responsibilities of a UX/UI designer: User research: UX/UI designers conduct user research to understand the target audience, their needs, and behaviors. Employing methods like interviews, surveys, and usability testing, they work to gather information that informs design decisions. Information architecture: UX/UI designers organize information to create intuitive navigation and efficient user flow. They create sitemaps, user flows, and wireframes to map out the structure and functionality of digital products. Interaction design: UX/UI designers define how users interact with a product by designing user pathways, interactions, and microinteractions. They create interactive prototypes using tools like Adobe XD, Sketch, or Figma to demonstrate the user experience. Visual design: UX/UI designers create visually stunning interfaces that align with a brand’s identity and evoke the desired emotional response. They work closely with color palettes, typography and visual elements, utilizing tools like Adobe Photoshop or Illustrator. Collaborative design: UX/UI designers collaborate with cross-functional teams, including product managers, developers, and stakeholders. They communicate design concepts and incorporate feedback, generating cohesive and user-centered interfaces. Accessibility: UX/UI designers consider accessibility guidelines and principles to make products usable by individuals with disabilities. They optimize designs for screen readers, keyboard navigation, and color contrast. Continuous improvement: UX/UI designers monitor user feedback, analytics, and market trends to make data-informed design decisions. They also work to stay updated with the latest design trends and emerging technologies. How do UX/UI Design and Data Analytics Differ? While both UX/UI design and data analytics fall within the realm of tech, they are distinct fields with unique objectives. Let’s identify some notable differences between these roles: Focus: UX/UI designers concentrate on the user experience. They work to craft functional and beautiful interfaces that please the user and drive targeted interactions . In contrast, data analysts aim to serve organizations and businesses, working behind the scenes to derive data-powered insights that improve metrics . Course history: UX/UI designers have often taken a UX/UI design course, earning them expertise in creating wireframes and prototypes using software like Figma or Adobe XD. On the other hand, data analyst courses focus on statistical analysis, programming, and proficiency in tools like SQL, R, or Python for data analytics and visualization. Audience: UX/UI designers interact primarily with clients, project managers, and other designers in order to build websites with the external audience in mind. Contrary to data analysts, who work with business analysts, data scientists, and stakeholders in order to improve operational efficiency or optimize marketing. Salary expectations: UX/UI designers can expect their salaries to differ slightly from data analysts. Data analysts in Spain, the Netherlands, and Germany earn slightly more on average. How do UX/UI Designers and Data Analysts Work Together? Collaboration between UX/UI designers and data analysts is not only possible, but highly beneficial for organizations. When working together, these professionals can combine data-led insights with impactful user experiences . Let’s take a look at a few ways UX/UI designers and data analysts can join forces: Data visualization: UX/UI designers are skilled in creating visual representations of data that are intuitive and easy to understand. Employing charts, graphs, and dashboards, they can represent complex findings in a clear and dynamic way . User-friendly interfaces: UX/UI designers can create simple-to-use interfaces for data analytics tools and platforms, allowing analysts to easily manipulate and store data. Through intuitive navigation, clear labeling, and responsive layouts, UX/UI designers can establish efficient pathways to new insights. Information architecture: UX/UI designers can help in re-structuring data analysis platforms to facilitate the ease of data exploration . They can create logical information hierarchies, design effective search functionalities, and optimize the overall organization to enhance the user experience of analysts. Collaboration and communication: UX/UI designers can bridge the communication gap between data analysts and stakeholders. By creating design artifacts such as wireframes and prototypes, they can help non-technical audiences visualize data findings in a more accessible way. Iterative design process: UX/UI designers can apply the iterative design process to data analytics tools by collecting user feedback, conducting usability testing, and refining the interface based on insights gained. This approach allows for continuous improvement and optimization of the tools, resulting in greater user satisfaction and productivity for analysts. It’s clear that UX/UI design and data analytics are contributing immense value to their respective domains. However, when these forces unite, they carry the potential to construct data-powered products that excel in both functionality and accessibility. As the digital realm continues to expand, the demand for these types of roles and collaborations will continue to soar. If you’re curious about a career in UX/UI design or data analytics, consider enrolling in one of our bootcamps; study remotely or in-person at one of our worldwide campuses.

The tech sector employs roughly 12.2 million workers in the United States alone, however, the tasks of each of those workers can differ significantly. For example, the responsibilities of a cybersecurity professional greatly differ from those of a data analyst, yet they’re both crucial to an organization’s success. Learning about other roles and what specific knowledge your coworkers possess will help you effectively collaborate and improve your organization’s performance. In short, a data analyst’s role is to organize, interpret, and extract valuable insights from data sets in order to support a company in making data-driven decisions. A cybersecurity professional, on the other hand, is responsible for the protection and securitization of a company’s computer systems, data, and networks from potential security threats. Cybersecurity plays a necessary role in data analysis by ensuring the securitization, confidentiality, and integrity of the data throughout the data analysis process. Their work undoubtedly overlaps and through collaboration , their distinct skill sets can aid better data securitization practices and lead to a more reliable, accurate, and secure data analysis process. What is Cybersecurity? We hear about cybersecurity all the time, but we often fail to see cybersecurity as an ongoing process and role that requires constant analysis and threat prevention work. In other words, cybersecurity is the process of protecting digital data, computer systems, and networks from unauthorized access, damage, theft, disruption, or other forms of ever-evolving security threats. Cybersecurity is an umbrella term used to describe a variety of processes that support the active monitoring, assessment, and preventative steps taken to safeguard information technology infrastructure and assets from malicious actors and potential vulnerabilities. A cybersecurity professional must work to ensure the confidentiality, integrity, and availability of digital data while promoting the overall security of computer systems and networks. The work of cybersecurity utilizes various measures, practices, and technologies in order to establish a strong defense against cyber threats . Cybersecurity requires one to predict the evolving terrain of potential cyber threats, identify organizational vulnerabilities, and implement controls to effectively mitigate said risks. There are numerous strategies and tools that can be used to test for threats, identify weaknesses, and better secure computer networks, systems, and digital assets . Of course the world of cybersecurity is always expanding, just as the terrain of the digital world equally morphs and transforms. That said, the key components of the work may change in relation to new demands and security threats. In other words, cybersecurity is an ongoing process that requires constant monitoring, analysis, and assessment in order to remain effective and robust in the face of evolving cyber threats. The work of cybersecurity is crucial for organizations to protect their digital assets and maintain the integrity of their systems. What Responsibilities do Cybersecurity Professionals Have? The role of a cybersecurity professional is to implement the work of cybersecurity, or better stated, to protect the integrity of computer systems, networks, and digital assets . It’s a role that requires a lot of foresight, awareness of industry developments, attention to detail, and strong analytical skills. The responsibilities of a cybersecurity professional will vary depending on the organization, however, a generally their work includes: Security planning and implementation: the bulk of a cybersecurity professional’s work is the development and implementation of security strategies, protocols, and procedures that protect a specific organization’s computer systems, networks, and data. This means risk assessment, analysis of vulnerabilities, and from that, designing the necessary security controls and measures. Threat detection: is the process of monitoring and analyzing networks and systems for potential security breaches, intrusions, or malicious activities. Cybersecurity professionals must utilize tools and technologies to best identify and address threats in real-time, with the intention of preventing or minimizing damage. Incident response and management: the development and implementation of incident response plans is crucial in order to effectively handle and mitigate security incidents. This includes investigating security breaches, repairing compromised systems, and restoring normal operations. Security auditing and assessment: cybersecurity professionals must conduct regular security audits, vulnerability assessments, and penetration testing in order to identify potential weaknesses in securitization systems and networks. These audits help test the effectiveness of security controls, and must be transformed in relation to new cyber threats, relevant regulations, and industry standards. Security architecture and design : in collaboration with IT teams, cybersecurity professionals will design and implement secure network systems and applications. These systems are designed in relation to security requirements, and include the implementation of encryption, access controls, and authentication mechanisms in order to embed security into the overall system design. Collaboration and communication : cybersecurity professionals must have strong collaboration and communication skills because their work often requires cross-functional teams, IT departments, and external partners. Equally, communication and coordination is necessary in providing guidance and responding to incidents effectively. Compliance and governance: cybersecurity professionals must ensure that their practices are in compliance with relevant laws, regulations, and industry standards. Research and innovation : addressing cyber threats in many ways is like shooting at a moving target because the threats to computer systems are constantly changing. That said, innovation is key for cybersecurity professionals as is staying up-to-date with the latest technologies, cybersecurity news, and security risks. Ongoing improvement: constant monitoring and continuous improvement is crucial to a cybersecurity professional’s ability to detect and respond quickly to security incidents. The commitment to ongoing improvement is crucial to the implementation of preventative measures. In all, a cybersecurity professional’s work is extensive. They need to have soft skills like collaboration and communication skills, as well as technical skills in order to design and create security systems that protect the integrity of computer systems and digital assets. Their work requires a lot of foresight, proactivity, strong analytical skills, and is ultimately crucial to an organization’s success in our increasingly digital and interconnected world. What is Data Analytics? Data analytics, on the other hand, refers to the process of investigating, organizing, transforming, and modeling data in order to gain actionable insights and make data-driven decisions that help organizations gain a competitive advantage. Their work is crucial in analyzing vast volumes of data in order to discover patterns and gain valuable insights that lead to a greater understanding of a particular problem or situation. Data analysts utilize various techniques and methods, including statistical analysis, data mining, machine learning, and data visualization . These data analysis tools enable organizations to extract meaning from raw data, and take action based on the information discovered. This could be data secured from an online database, or collected based on a consumer’s behavior on an app, their demographic information, or through securing consumer feedback and preferences. Through knowing and analyzing this information, data analysts can make data informed recommendations on how to offer a stronger product or service . Ultimately, data analytics is necessary for businesses who want to work smarter, not harder. Through using data analytics, companies can enhance offerings, navigate challenges effectively, and make informed decisions. What Responsibilities do Data Analysts Have? A data analyst’s role is to collect, organize, process, interpret, and extract important insights from a set of data. The goal is to use those findings to inform organizational decision-making, ensuring that a business is deliberate and intentional in their decisions. Their main responsibilities include: Data research and collection: it's often the data analyst’s role to research and gather data from various sources. These sources may include databases, spreadsheets, APIs, or external datasets. Data preparation: once data is secured, it must be organized into an easy to understand format such that relevant data can be easily analyzed. This means removing errors, inconsistencies, or missing values. Exploratory Data Analysis (EDA) : is a data analysis method that helps with the initial exploration of data in order to identify basic patterns, trends, correlations, and anomalies. EDA may require descriptive statistics, data visualization techniques, and data querying. It’s often carried out utilizing python functions in order to explore data. Data visualization : a data analyst should create visual representation of data, such as charts, graphs, or dashboards, to effectively communicate findings and insights to stakeholders. The communication and reporting of the data is crucial, we have a couple tips here for how to best present that data. Data-informed decision making: the ultimate work of data analysts is to put all their findings into practice through making data driven decisions for the organization. These could be decisions surrounding areas for improvement, ways to optimize processes, or strategic decisions based on one's expanded knowledge surrounding their consumers or product. Data security and privacy : more than ever, securing data sets is a commitment to protecting sometimes sensitive information and is crucial to the integrity of a company. Data security is necessary in ensuring that data is only accessed by authorized users. In sum, a data analyst plays a hugely important role in transforming raw data into actionable insights, supporting organizations to make informed decisions, improve their products or services, effectively solve problems, and reach their potential. How do Data Analytics and Cybersecurity Differ? Data analytics and cybersecurity are distinct fields, although they undoubtedly have numerous transferable skills and complete each other’s work in certain aspects. That said, the focus and objectives of the fields , as well as their core activities, skill sets, and areas of application are vastly different. Briefly, data analytics focuses on extracting insights and meaning from data , while cybersecurity is centered around protecting computer systems, networks, and data from unauthorized access. While a data analyst’s core activities include collecting, processing, cleaning, and analyzing data , a cybersecurity professional works to assess risk, implement relevant security measures, monitor systems for threats, and respond to security incidents. Although their work can be complementary, their core responsibilities greatly differ. How do Cybersecurity Professionals and Data Analysts Work Together? Despite being relatively distinctive roles, cybersecurity professionals and data analysts should work together in the process of securing data and ensuring the confidentiality of data throughout the analysis process. Their collaboration can improve the integrity of the organization, prevent data breaches and other threats to network security, and promote a more reliable data analytics process . Their collaboration ultimately grants more validity to the insights derived from the data analytics process. Cybersecurity professionals should aid the data analytics process in the following ways: Promoting data privacy and confidentiality: cybersecurity professionals are crucial in the process of safeguarding sensitive data during data collection, storage, and analytics. This work includes implementing access controls, encryption, and data anonymization techniques to protect personally identifiable information (PII) and other confidential data. By maintaining data privacy, cybersecurity professionals play an essential role in supporting the ethical and legal practices of data analysis. Ensuring data integrity: integrating cybersecurity practices into data analysis means that the data will remain accurate, complete, and unaltered during the collection, storage, and analysis. Through the inclusion of measures like data validation, checksums, and digital signatures, cybersecurity professionals ensure that data remains untampered and unauthorized modifications are prevented. Securing data storage and transmission: involving cybersecurity professionals in the process of storing and transmitting data is important. This includes employing storage systems and encryption techniques to protect data at rest and in transit. One may utilize secure transmission protocols like HTTPS or VPNs in order to encrypt and protect data as it is transferred between systems or across networks. Detecting threats : monitoring data analysis systems and networks for potential threats and intrusions is crucial to safeguarding data. This could be achieved through the implementation of intrusion detection systems (IDS), firewalls, and other security technologies. Ultimately, timely detection and mitigation of threats helps maintain the reliability and accuracy of data analysis results. Assessing vulnerability: carrying out regular vulnerability assessments and penetration testing is essential in uncovering weaknesses in data analysis systems. Cybersecurity professionals conduct these assessments to proactively identify and confront potential vulnerabilities that could be targeted by malicious actors. Quickly addressing vulnerabilities helps to ensure the security and reliability of the data analysis infrastructure. Incident response and recovery: if tech professionals find themselves in the unfortunate position of experiencing a security breach, cybersecurity professionals are responsible for the incident response procedures. They must investigate the incident and restore the affected systems quickly in order to minimize disruptions to data analysis processes. Ensuring quick incident responses helps managing the integrity and availability of data used in analysis. These are just a few of the many ways in which cybersecurity professionals should collaborate with data analysts in order to ensure a reliable, secure, and accurate data analysis process . Integrating cybersecurity techniques in the management, storage, and transmission of data is essential to safeguarding sensitive data during the collection, storage, and analysis . Through collaborating, organizations can be sure that their data analysis is sharper, more reliable, and secure from potential cyber threats. Looking for a data analytics course or gaining new skills through a cybersecurity bootcamp ? Look no further. If you’re interested in learning more about cybersecurity or data analytics, check out the Ironhack bootcamps! Signing up for a bootcamp may be all that you need if you’re hoping to secure a cybersecurity or data analyst job.

You might think that choosing one specialization in tech severely limits your interactions with other branches. After all, if you choose to become a web developer, you’ll be working with web development exclusively, right?! Well, not really. One of the many benefits that the tech sector offers is the ability to work across a multitude of areas, relying on help from experts in other areas to share their knowledge. Two areas that frequently interact are web development and data analytics and you’ve probably already checked out our article on the role that data analytics plays in web development , but we’re going to flip it around and talk about the role that web development plays in data analytics. Sounds good? We hope so! But as always, let’s first define both data analytics and web development so that you’re completely sure of each role. What is Data Analytics? Broadly speaking, data analytics is the study of large amounts of data to then make insightful decisions . It consists of inspecting, cleaning, transforming, modeling, interpreting, and visualizing data so that data analysts can draw data-backed conclusions which will then help them make better decisions overall. Within data analytics as a whole, we can divide it into three categories: descriptive, predictive, and prescriptive analytics. Descriptive analytics : using data aggregation and data mining, analysts using descriptive analytics look exclusively at past data, looking to understand past decisions and their effects. Predictive analytics : using probability, predictive data analytics use statistical modeling and machine learning together with historical data to predict what will happen in the future and guide business decisions along with that. Prescriptive analytics : instead of merely showing results, prescriptive analytics is used to provide a specific and actionable solution from analyzing both historical and present data. When used properly, data analytics can absolutely transform a company’s results, accurately predicting what clients will want next and optimizing processes. On a broad, overhead level, companies can benefit from the following when implementing data analytics: Improved employee productivity, efficiency, and engagement : by analyzing business actions and seeing what areas are redundant and where time can be saved, companies can save both time and resources, directing employee efforts to the areas that really matter. Tightened cybersecurity : data analytics isn’t limited to just making predictions about the future! In fact, predictive analytics can help detect fraud, launch automatic warnings, and make sure that real-time threat detection tools are working properly. Better customer experiences : clients share their data with companies to receive an improved and personalized experience; by looking at what customers like, want to see, and not working can help improve the overall experience. Faster launch of new products or services : no company is going to launch a new product without properly testing it beforehand; part of this testing is analyzing past data to see when the best time to launch something is, what customers are looking for, and what works best with the target audience. When all of these have answers from data, it makes the product launch process much easier. Better decisions based on data: data analytics doesn’t just mean looking at past data to predict how the future will go; you can predict the success of certain actions by looking at predictions and then make the decision. With both historical data and future estimates, your decisions will be much better. Now that you’re a data analytics expert, let’s dive into web development and see how the two work together. What is Web Development? When you hear about tech, and especially coding, your mind might jump right to web development. And that’s completely understandable! After all, web development is a major part of tech and creates the backbone of the internet: the structure and make-up of websites. Using various programming languages, tools, and techniques, web developers create, maintain, and optimize websites. The vast majority of web developers and web development tasks fall into three categories: front end development, back end development, and full stack development. Front end web development: simply put, front end development handles the part of website creation that users will eventually see and interact with; it’s the role of front end professionals to create user-friendly and responsive websites through HTML, JAvaScript, and CSS. Back end development : this is the more technical side that users will never see; this is where data is handled and stored. Back end developers use programming languages such as PHP and Java to write the code that supports the website. Fullstack development : both back and front end development are required knowledge for fullstack developers who create the website as a whole, taking on both front and back end responsibilities. These are definitely the three most common roles, but you can also run into web designers, web programmers, content developers, and webmasters. But no matter your chosen role, high-quality web development is absolutely crucial for a company’s online success and here’s why: It creates a better website : it might seem too obvious, but clients are more likely to return to websites that are easy to use, clear, and visually appealing. It attracts new customers : with a high-quality website that works quickly and directs clients to what they’re looking for, companies will also attract new audiences. It allows you to make edits accordingly: well created websites will allow back end developers the ability to make quick changes if any information is adjusted, promising true and up-to-date information to customers. What Responsibilities do Web Developers Have? Web developers are responsible for the construction, maintenance, and upkeep of both online websites and applications. Exact responsibilities vary depending on if you’ll focus on the front or back end, but generally speaking, web developers will be responsible for: Solving any website problems or bugs Working with UX/UI designers to ensure the design aspects of the website match the back end coding Ensuring that the websites are safe and securely store user and company information Using programming languages like HTML, CSS, Python, JavaScript, and more to construct the website’s foundations Monitoring website traffic to see which pages are receiving the most visits Optimizing website aspects that are not working as planned Staying up-to-date with what’s going on in technology How do Web Development and Data Analytics Differ? This is an easy one: they’re totally different! As you’ve probably imagined from the rest of this article, web development and data analytics are completely different fields of technology and the skills required for both are diverse. Let’s explore a few of the main differences: Web developers rely on coding and programming to do their job; data analysts use their knowledge of statistics and mathematics to make sense of data derived from websites. Projects for data analysts tend to have a short span, seeking to deliver actionable results in a specific window of time. Web development projects, on the other hand, are long and detailed projects that can last for years as optimizations and improvements are implemented. Looking towards the future, we can expect that machine learning and artificial intelligence will have a major impact on the field of data analytics. Advances in e-commerce and e-learning, however, will play a large role in the future of web development. How do Data Analysts and Web Developers Work Together? Although we just finished explaining how these two fields are quite different, we have a little secret: data analysts and web developers do actually work together , looking to use data to make smart decisions when it comes to web design.  Not sold on the idea? Let’s give you an example. Say a web development team has been tasked with creating a dog walking app. Before the developers even begin their planning, they’ll meet with data analysts to understand why they’re creating this kind of website, their goals, and important things to keep in mind, such as knowing that the dog walking app needs to hold personal data and allow for secure payments. Once the website has been launched, data analysts will then come to the development team with information about bounce rates, areas for optimization, data on competitors, and anything else they’ve learned from checking out data. The roles of web developers and data analysts are different, we’re not going to argue that! But when they work together, they can complement the responsibilities of the other, churning out an all-together superior final product. Both web development and data analytics are fantastic fields with lots of opportunities, room for growth, lucrative salaries, and interesting projects. No matter which you choose, we’re sure you’ll reach success in no time; check out our Web Development and Data Analytics Bootcamps today.

In today’s interconnected world where our personal and professional lives rely heavily on technology, the importance of online security cannot be overstated. While the realm of cybersecurity continues to evolve, there is a parallel world that operates in the shadows--the dark web. Contrary to its name, the dark web itself is not inherently malicious , rather, it is the activities and actors it harbors that make it a hotbed for cybercrime. Before we get into ways to stay safe, let’s first understand the threat the dark web poses to cybersecurity professionals and individuals alike. What are Dark Web Hackers? The dark web is a hidden part of the internet that requires special software to access and provides anonymity to its users. Within this clandestine realm, hackers operate, connecting and collaborating through various dark web hacking forums and communities. This provides a platform for hackers to enhance their skills, seek assistance, and engage in illicit activities. Let’s take a look at some of the motives and targets behind dark web hacking. Motives of hackers Financial gain: dark web hackers are typically motivated by monetary incentives. They exploit vulnerabilities in systems to gain unauthorized access, steal sensitive data, and monetize it through various means, such as selling the data on underground marketplaces or conducting ransomware attacks. Espionage and cyber warfare: state-sponsored hackers often operate within the dark web to conduct cyber espionage and surveillance. They target government agencies, critical infrastructure, and industries of strategic importance in order to disrupt operations or gain intelligence. Hacktivism: “ hacktivist” hackers are driven by ideological or political motivations. They target organizations or individuals that they perceive to be engaging in unethical activities, with the objective to disrupt or expose their targets to promote a specific cause. Personal vendettas: dark web hackers sometimes engage in hacking for personal reasons, seeking revenge or retaliation against individuals or organizations that they believe have wronged them. Common targets of hackers Individuals: individuals are often targeted by dark web hackers for personal financial gain. Hackers may exploit vulnerabilities in personal devices or use social engineering techniques to gain access to personal information, bank accounts, or social media profiles. Businesses: companies of all sizes are attractive targets for dark web hackers due to the potential financial rewards. They exploit vulnerabilities in networks, applications, or employees to steal valuable intellectual property, customer data, or conduct ransomware attacks. Government agencies: administrations may fall victim to hackers with a political agenda or state-sponsored backing. Classified intelligence, sensitive diplomatic communications, or strategic plans are often leaked through dark-web hacks. Financial institutions: banks are also subject to attacks from hackers who aim to gain access to customer data, financial transactions, or exploit weaknesses in banking systems for monetary gain. Common hacking techniques Dark web hackers employ a range of sophisticated techniques to breach security systems and compromise sensitive data. By understanding these methods, individuals and organizations can better defend against potential cyber threats . Let’s explore some common hacking techniques, along with examples of how they might be executed. Social engineering Hackers exploit human vulnerabilities to deceive individuals and manipulate them into revealing confidential information or gaining unauthorized access. Hackers leverage various tactics, including: Phishing: hackers send deceptive emails or create fake websites that mimic legitimate organizations, tricking individuals into disclosing sensitive information such as passwords or credit card details. Example: a hacker sends an email posing as a bank representative, urging the recipient to click on a link and provide their login credentials to prevent account suspension. Pretexting: hackers create a fabricated scenario to gain the trust of their targets and extract personal information. This technique often involves impersonating a trusted authority figure. Example: a hacker pretends to be an IT technician and calls an employee, claiming they need their login credentials to resolve a technical issue. Baiting: hackers offer something enticing, such as a free software or a prize, to lure individuals into taking actions that compromise their security. Example: a hacker promotes a fake online contest promising a luxurious vacation package that requires users to submit their personal information to enter. Ransomware: hackers leverage various forms of malware to encrypt victims’ files and demand a financial ransom for their release. Example: a hacker exploits a vulnerability in outdated software to gain access to a company’s servers, and extorts them for money in exchange for not corrupting important data. Malware attacks Making use of malicious software, dark web hackers infect systems, steal data, or launch further attacks. Let’s look at some types of malware used in these attacks: Viruses: malicious programs that infect files and populate throughout the system, corrupting data or disrupting functionality. Example: a hacker sends an infected email attachment that, once opened, installs a virus that gives the attacker access to the recipient's computer. Worms: self-replicating programs that spread across networks and work to infect multiple systems. They can cause significant damage by consuming network resources and compromising data. Example: a hacker exploits a vulnerability in a network service and releases a worm that rapidly spreads to other devices within the network, disrupting operations. Trojans: inconspicuous programs that pose as legitimate software or files, but are actually malware. This allows hackers to gain access to a system while appearing harmless to the user. Example: a hacker creates a seemingly innocent application that, when downloaded and executed, opens a backdoor on the victim’s computer, providing the attacker with remote access. DDoS attacks Distributed Denial of Service (DDoS) attacks involve overwhelming a target’s system or network with a flood of traffic, rendering them inaccessible to legitimate users. Let’s look one way DDoS attacks are launched: Botnets: hackers infect numerous computers or devices with malware, creating a network of compromised machines called a botnet. They then use this collection of devices to send a massive wave of traffic to the target, overwhelming its resources. Example: a hacker infects thousands of computers with malware, turning them into “zombies” that are controlled remotely and used collectively to inundate a website’s server, rendering it inaccessible. Password cracking Dark web hackers employ various techniques to crack weak or stolen passwords, giving them access to systems or accounts. Here’s a few common ways that it’s done: Brute-forcing: hackers apply automated tools that systematically try every possible combination of characters until the correct password is found. Dictionary attacks: hackers utilize a pre-built list of common words, phrases, or passwords to efficiently guess the correct password. Credential stuffing: hackers wield stolen username and password combinations from one website or data breach and try them on other websites, exploiting individuals who reuse passwords across multiple platforms. Strengthening Your Defenses Against Dark Web Hackers Now that we understand the threats hackers pose against individuals and organizations, it’s time we get into how to fortify your defenses. By implementing robust preventative measures and security practices, these nefarious dark web actors can be kept at bay. Here’s some key strategies to defend against cyberattacks: Cultivate a culture of cybersecurity awareness: building a strong foundation of cybersecurity and dark web awareness is vital for individuals and organizations. Educate yourself and your employees about the latest hacking techniques, conduct or participate in regular training sessions around system security, and learn to spot suspicious emails and websites. Implement multi-factor authentication (MFA): adding an extra layer of security makes gaining access significantly more challenging for hackers. This could mean using a unique verification code sent to a mobile device, biometric data, or hardware tokens. Update software and systems: updating software and systems regularly is crucial for staying protected against vulnerabilities. Dark web hackers often exploit security flaws in outdated software to gain access. Enable automatic updates for operating systems, applications, and security patches. Employ robust endpoint security solutions: safeguarding your devices and networks with antivirus software, firewalls, and intrusion detection systems help to block malware and ransomware before they can infiltrate. Conduct regular data backups: organizing a regular backup strategy ensures that critical data is protected in the event of a cyberattack. Store backups in secure off-site locations or use cloud-based backup solutions, and regularly test the restoration process to verify the integrity and accessibility of backups. Encrypt communications: implementing end-to-end encryption for sensitive communications adds additional security, making it much more difficult for hackers to intercept and decipher confidential information. Monitoring the dark web Advances in dark web cybersecurity provide an opportunity for early detection of data breaches and leaks. Cybercriminals often trade stolen data such as login credentials and sensitive documents on dark web marketplaces. By utilizing monitoring platforms such as Recorded Future, Digital Shadows, or SpyCloud, individuals and organizations can identify if their data has been compromised and take immediate action. Let’s look at a few ways that individuals can utilize cybersecurity companies’ dark web monitoring to their benefit: Proactive threat intelligence: by scanning dark web hacking forums and marketplaces, individuals and organizations can stay informed about emerging threats, new hacking techniques, and potential vulnerabilities. This enables proactive measures to be taken and reduces the risk of falling victim to cybercriminals. Insider threat detection: by supervising employee-specific data on underground forums or illicit sites, organizations can detect if employees are involved in selling sensitive information or colluding with cybercriminals. This allows for organizations to take the appropriate action, conduct internal investigations, and provide additional training. Vendor risk management: by monitoring the dark web for mentions of vendors’ names or leaked credentials, organizations can identify potential risks associated with their supply chain. This information allows them to make informed decisions about engaging with vendors and implement necessary security measures to protect their data. Now that we’ve established the essential role of cybersecurity in our lives and organizations, it’s important that we remain vigilant. Empowered with knowledge and fortified defenses, we have the ability to safeguard our digital lives. To stay informed about the latest cybersecurity trends and practices, we invite you to check out Ironhack’s Cybersecurity Bootcamps, or explore more articles on our blog.

As the tech landscape continues to shift, keeping up to date with the latest tools and practices is essential for any web developer. One of the most in-demand skills of the moment is DevOps, a method of combining IT and software development that’s transforming the industry. Before we jump into the key benefits and pathways of learning DevOps, let’s take a closer look at what it is and why it’s in such high demand. What is DevOps? DevOps has gained immense popularity in the web development field due to the numerous benefits it brings to the software development lifecycle. In short, it’s a set of practices that combines software development (Dev) and IT operations (Ops) to streamline the software delivery process . By breaking down the traditionally siloed teams of development and operations, DevOps promotes a culture of continuous integration, delivery, and improvement. Let’s take a look at some of the benefits of DevOps and learn why companies are quickly incorporating it into their arsenal: Increased collaboration and communication: DevOps encourages developers and operations personnel to work together closely throughout the entire software delivery process. With the ease of cross-department communication comes faster issue resolution, improved efficiency, and reduced time-to-market. Continuous integration and delivery: DevOps enables developers to merge code changes more frequently and with less risk of error . Automated testing and deployment processes ensure that software updates can be released to production with reliability; this leads to greater customer satisfaction and faster delivery speed. Enhanced quality and stability: DevOps integrates quality assurance into the development process from the beginning. Code reviews, testing, and frequent deployments to staging environments help catch bugs early on, leading to high-quality software. Continuous monitoring and feedback loops allow for real-time visibility into application performance , enabling rapid response and improvement. Cost optimization: DevOps promotes automation and efficient use of resources, which can lead to greater cost savings. By automating repetitive tasks and optimizing infrastructure , companies can reduce operational expenses. Additionally, the use of cloud services can allow for flexible resource allocation, so companies only need to pay for the resources needed. Continuous learning and improvement: DevOps embraces a culture of continuous improvement, where feedback, monitoring, and analytics drive learning and innovation . By regularly reviewing performance metrics, gathering user feedback, and identifying areas for enhancement, DevOps leads to more robust and reliable web applications. Learning DevOps Now that you see the benefits that DevOps can bring to companies and teams, it’s high time we discuss how to acquire these sought-after skills. Let’s outline some of the essential steps in becoming a DevOps professional: Understand DevOps principles: familiarize yourself with the fundamental role of DevOps, and how it benefits the software development life cycle; this is not only important for your understanding, but also to communicate the value of these newly-acquired skills to potential employers and non-tech clients. Learn version control systems: become adept with systems like Git, which are fundamental tools for code management in DevOps. Utilize Git effectively to create/use repositories, merge or branch code, and resolve conflicts. Understand concepts like pull requests and code reviews, which promote collaboration and code quality. Gain proficiency in DevOps automation tools: explore popular automation tools such as Jenkins, Bamboo or CircleCI, and learn how to automate tasks such as building, testing, and deploying applications. Understand the concepts of continuous integration, delivery, and deployment. Acquire infrastructure-as-Code (IaC) skills: learn tools like Terraform or CloudFormation to provision and manage infrastructure resources; this is a crucial aspect of DevOps, because it enables you to host codified infrastructure templates to speed up workflow. Enabling scalability and consistency in your infrastructure deployments saves time and reduces the risk of error. Dive into containerization: investigate the benefits of containers, particularly Docker, that have revolutionized software development by packaging necessary files together . Explore container orchestration platforms like Kubernetes to understand how containers can be managed and deployed at scale. Familiarize yourself with cloud platforms: become proficient in cloud computing platforms like AWS, Azure, or Google Cloud, which are integral to modern DevOps practices. Understand how to use cloud services for hosting applications, managing infrastructure, and leveraging services such as serverless computing, databases, and storage. Practice continuous monitoring and logging: learn how to set up monitoring systems, configure alerts, and collect metrics by using tools like Prometheus, Grafana, or ELK Stack (Elasticsearch, Logstash, Kibana). Realize the importance of logging and how it helps with troubleshooting and performance optimization. Embrace collaboration and communication: foster your communication skills, welcome teamwork, and promote cross-functional discussion; these are key principles of DevOps, and will serve you as you work closely with both development and operations teams. Seek hands-on experience: apply your DevOps knowledge to real-world projects; implement DevOps practices to your existing projects, further collaborate with teams, or even join open-source projects. Gaining practical experience will help solidify your understanding and build confidence in applying DevOps principles. Enroll in bootcamps: consider pursuing online or in-person training programs that offer comprehensive DevOps curricula. Look for reputable bootcamps that offer practical training and the opportunity to collaborate. Completing courses validates your skills and demonstrates your commitment to DevOps practices. What is a DevOps Engineer? The role of a DevOps engineer is to design, implement, and manage the infrastructure and tools necessary for continuous integration, delivery, and deployment of software applications. A DevOps engineer is responsible for automating processes, optimizing systems, and ensuring efficient collaboration between development and operations teams. Let’s take a look at some specific types of DevOps engineers, and the DevOps certifications necessary to achieve these positions: AWS Certified DevOps Engineer: focuses on managing DevOps practices on the Amazon Web Services (AWS) platform. AWS engineers utilize AWS CodePipeline and AWS CloudFormation to build and maintain highly scalable architectures, automate deployment, and ensure secure operations. Key qualifications include deep knowledge of AWS services, proficiency in infrastructure-as-code (IaC) tools, and expertise in AWS tools like AWS CodeDeploy and AWS CodeStar. Certification: become certified through passing the exam through AWS. The exam includes 75 questions and it takes approximately three hours to complete. Microsoft Certified: Azure DevOps Engineer: specializes in the Microsoft Azure cloud platform, utilizing Azure DevOps tools and services to implement end-to-end workflows. To excel in this role, candidates should possess strong knowledge of Azure IaC tools like Azure Resource Manager (ARM) templates and feel comfortable leveraging Azure specific tools Azure Pipelines and Azure Boards. Certification : become certified by first completing one prerequisite Azure exam-certification. The final engineer-certification requires candidates to demonstrate their competence in using Azure over five different DevOps categories. Google Cloud DevOps Engineer: wields the Google Cloud Platform (GCP) to facilitate DevOps practices. Essential qualifications for this role include masterful use of GCP services, experience with IaC tools like Terraform, and the ability to leverage GCP tools such as Cloud Build and Cloud Deployment Manager. Certification: become certified by passing the exam through GCP. The exam consists of 50-60 questions and is around two hours in length. DevOps-related jobs and certifications Outside of specific DevOps engineer jobs, the demand for various skilled professionals in the field continues to skyrocket. Let’s delve into some other DevOps-related job roles and touch on their respective certifications: Release Manager: the role of a release manager is to coordinate and oversee software releases, ensuring that they are delivered smoothly and efficiently. They collaborate with development, operations, and quality assurance teams to plan, schedule, and execute releases. Some certifications for release managers include: ITIL Foundation: this certification, although not DevOps-oriented, proves a comprehensive understanding of IT service management, including release management best practices. Certified ScrumMaster (CSM): this certification focuses on the Agile framework, which is widely adopted in DevOps environments. It equips professionals with the knowledge to facilitate collaboration, communication, and efficient release management within Agile teams. Automation Architect: the role of an automation architect is to design and implement automated workflows, ensuring efficient and reliable deployment processes. They leverage various tools to streamline repetitive tasks, eliminate manual intervention, and optimize the software delivery pipeline. Likely certifications for automation architects include: Puppet Certified Professional : this certification validates expertise in using Puppet, a popular configuration management tool, to automate and manage infrastructure deployments. Ansible Certified Engineer: this certification demonstrates skills in using Ansible, an open-source automation tool, for configuration, deployment, and orchestration. Site Reliability Engineer (SRE): the role of SREs focus on ensuring the reliability, availability, and performance of software systems. They work closely with development and operations teams to design and implement resilient architectures, monitor system performance, and proactively address incidents and outages. Recommended certifications for SREs include: Certified Kubernetes Administrator (CKA): this certification displays competency in managing and scaling containerized applications using Kubernetes, a platform for container orchestration. Certified Site Reliability Engineer Foundation: this certification provides an understanding of SRE principles, practices, and tools for optimizing system reliability and performance. I hope we’ve made it clear that web developers stand to gain immense benefits by learning DevOps. By investing in sought-after skills, you can position yourself as a versatile professional that can help companies streamline the entire software development cycle. Looking to enhance your web development career, or just get more hands-on experience? Consider enrolling in one of our bootcamps and watch your career kick into high-gear.

We don’t need to stress the importance of accessibility in today’s world: it’s absolutely crucial and something that more and more people are adopting around in practically every industry. But as the world becomes increasingly dependent on the internet and digital services, (after all, some companies have stopped offering non-digital services altogether!) ensuring that the web is an accessible place is crucial. Before we dive right into how to make accessibility-friendly designs online, we think it’s a good idea to first chat more about accessibility, why it matters, who it affects, and common struggles that people face when accessibility isn’t a priority. What is Accessibility? Generally speaking, accessibility is defined as making sure something, be it a product, service, experience, information, or anything else, can be used by absolutely everyone, regardless of their personal situations. Typically used to only refer to people with disabilities, the importance of accessibility and ensuring equal access to everyone is not a new concept, but since the internet is quite new, it’s essential that the next generation of techies is well-prepared to create inclusive and accessible designs. Some of the most common reasons for needing accessible services might refer to people with disabilities, such as visual, hearing, motor, or cognitive ones, but creating truly accessible web designs goes beyond that. When designing websites that are supposed to be accessible to children, for example, designers must put themselves in the shoes of children and design attractive, interesting, and colorful sites that direct children where they need to go. On the other hand, a senior citizens’ website shouldn’t have small text or overly technical information. Okay, we gave away what we’re going to talk about in the rest of the post! So before we dive into making accessible designs for each of the aforementioned areas, let’s tackle another important matter: the importance of accessibility. Why is accessibility important? It’s quite simple: accessibility allows everyone, regardless of their age, economic status, disability status, or gender, to access the same services. When it comes to publicly offered services, such as immigration or healthcare, it’s even more crucial that everyone can not only access the information, but also understand their rights. Allowing everyone the chance to be active and participating members of society is something we should strive for in absolutely every area, including web design. Accessibility in Web Design As we mentioned above, the internet’s importance has absolutely skyrocketed, especially after the COVID-19 pandemic. Suddenly, practically everything was online and those with diverse circumstances were sometimes unable to access the same information or services as their peers. That’s why accessibility in web design is so crucial; the internet is a tool that can be used to spread lots of information, learn, and connect, but only when the experience yields the same outcome for all users. For something to be accessible online, it needs to be : Perceivable : the information needs to be apparent to the user and available to one of their senses; for example, if a website is made up of text, there needs to be an audio option to include blind users. Understandable : the content provided needs to be understandable to the chosen audience; if you’re creating a website for immigrants, ensuring that the information is available in various languages can make the content understandable. Operable : if someone is only able to control their computer via voice, the website should work the same as it does for someone who can only use a mouse or a keyboard, therefore being operable. Robust : the website must function the same as it does for a person without disabilities, meaning that no one has a worse experience. Are accessibility and usability the same thing? People frequently use accessibility and usability interchangeably, but there are some key differences between the two. They have similarities, yes, but usability focuses on the effectiveness, efficiency, and satisfaction of a design; accessibility, on the other hand, prioritizes knowing that all users, regardless of their disability, can properly use the design. Accessible Web Designs Now that we’ve covered what accessibility is and why it’s so crucial, especially in web design, let’s dive a little deeper into specific cases where accessibility is needed and give some suggestions. We’re going to begin with specific disabilities that users will have, but also cover some common ability barriers that are sometimes forgotten. Important note: as with anything, the levels of a person’s disability vary significantly. Someone may be completely blind or just unable to see colors; a Deaf person may communicate solely by sign language or rely on lip reading. Visual accessibility When creating a web design, keep in mind that those who can’t easily distinguish between similar colors may have trouble with subtle changes; here’s a great example of when you need to incorporate multiple elements into your design. If there’s a mistake, use both the color red and the word “Error” to communicate that to your user. For users who are completely blind, having accessibility options to hear a reading of the webpage instead of reading it can be a total game-changer. But be careful; if your webpage has a lot of pictures with images or a menu, for example, as an attached document, make sure that there’s an accessible version that can read that information to the user as well. Hearing accessibility Creating an accessible design that’s of use to Deaf or hard-of-hearing users requires planning; using captions on videos that are reviewed, synchronized, and accurately display what’s being spoken in the video is essential. And if your only method of contact is by the phone, consider adding in an email or chat option so that those who can’t communicate by the phone have equal access to contacting your company. Motor accessibility Those facing motor impairments might face tremors, involuntary movements, paralysis, lack of coordination, or missing limbs, all of which can make using a website quite difficult. Thankfully, we’ve come a long way in this area, creating custom keyboards with more space between the keys or different kinds of mouses. But when it comes to the website itself, make sure all functions are reachable by the keyboard (not the mouse!) and it’s easy to incorporate software that helps the user navigate the website. Cognitive accessibility Frequently forgotten when talking about accessibility, folks with cognitive impairments also benefit from a personalized web experience. Ensure that your site is cleanly-designed, with easy to read and access buttons and tabs. A user with dyslexia, for example, might be deterred by large amounts of text. Offer information in varied formats, such as infographics, videos, or more, and keep the language clear and simple. Environmental accessibility Have you considered that someone might need to access your services from a place where there’s no WiFi or cell service? Or from a public place, such as a library, where they can’t enter personal data? Offering offline services and the ability to browse without entering identifying information can make your site more inclusive. Educational accessibility You can probably remember a time when you entered a website to research something, saw tons of technical jargon, and immediately went back to the search page. Not everyone has the same level of understanding regarding certain topics and creating content that’s overly dense or technical is basically excluding those who haven’t studied it extensively from accessing that information. Use simple, explanative language and go into detail when necessary. This is also extremely relevant when creating content for children. We’re not just talking about fun websites for games; imagine you’re working on creating a website where children learn about how to call emergency services in the case something goes wrong. If you display and write the information as you would for an adult, it’s very likely that the child won’t be able to understand the crucial details. Use pictures, include video or audio options, and child-friendly wording that makes the content truly accessible to all. Language accessibility We touched on this above but guaranteeing that your users, regardless of their native tongue, have the same access to services and information should be your priority. Especially when it comes to government, immigration, or refugee services, providing high-quality translations, video captioning, and specific contact methods per language will include an entire population that was previously unable to access that service. Incidental accessibility This is probably the type of accessibility that’s least considered, but is just as important. Have you ever had to answer the phone in the middle of the night and been almost blinded by your screen’s blindness? Or accidentally hit the wrong button in your sleepiness, losing your place in your article? Offering users the option to lower brightness, increase font size, or use black and white settings can make your design more digestible. Tips for Improving Web Accessibility If the aforementioned points have convinced you of the need to include accessible designs in your next project, take a look at some of our tips to ensuring your next web design is truly accessible: Include people with various disabilities, ages, and educational levels when creating your buyer personas Always include alt-text on images or graphics Research color selection and font size before making a decision, or include the option to adjust as needed Ensure your site is mobile-friendly Offer quality transcriptions and captions for videos and anything that has audio Use clean and simple designs and language Creating accessible content is an absolute necessity and once you truly commit to creating accessible web designs, your audience will expand and more and more people will be able to benefit from your product or service. If you’re ready to take the next step and dive right into creating high-quality and accessible web designs, Ironhack’s UX/UI Design Bootcamp is the perfect choice for you.

There are lots of buzzwords out there: machine learning, deep learning, and much more. And while they may sound similar, they actually differ considerably, and we’re here to define both, talk about their similarities and differences, and make sure that you’re an expert by the end of this article. But before we get there, it’s important to understand that both machine learning and deep learning are two approaches to enabling artificial intelligence. What is Machine Learning? Let’s get right into it: what’s our machine learning definition? Machine learning is a relatively broad term that encompasses methods designed to enable artificial intelligence using data. Machine learning algorithms generally fall under two categories: supervised learning and unsupervised learning. In both cases, large quantities of data are used to train models to extract meaningful patterns and relationships. The trained models can then be applied to new data or tasks. Machine learning encompasses various algorithms and techniques, including the set of techniques that we refer to as “deep learning.” For the purposes of this article (and, indeed, this is how the two terms are used colloquially), we will consider machine learning and deep learning to be two different approaches to training models to make predictions and/or decisions based on data. Real life examples of machine learning Machine learning is used way more often than you might think! It used to be a niche skill, but with the advent of powerful tools such as PyTorch and TensorFlow, many more software engineers and data scientists have begun to use machine learning techniques in their projects. Here are some examples of how machine learning is making our lives better every day: Spam detection : machine learning models can analyze email content to classify emails as either spam or non-spam, helping you to filter out unwanted messages from your already crowded inbox. Recommendation systems : are you on social media? Do you use any streaming apps? If so, your preferences and behavior are being used by machine learning models to give you better recommendations. Medical diagnosis : doctors now have access to tools that can take in symptoms, test results, and imaging to assist in disease diagnosis. How cool is that? Fraud detection : banks employ machine learning models to constantly analyze user accounts and transactions for unusual behavior in order to prevent fraud such as identity theft or credit card scams. What is Deep Learning? Neural networks and deep learning go hand-in-hand. Deep learning is a subset of machine learning that uses tools called artificial neural networks to learn from data. Neural networks use multiple interconnected layers of artificial neurons (continuing the brain metaphor), or nodes, that are trained to synthesize higher-level patterns in data. Deep learning is seen as a more powerful form of machine learning as it can be used to extract meaningful insights from larger datasets and used to solve more complex problems—but deep learning models also require significantly more computational power and skill to train. Real life examples of deep learning We use deep learning when the problem is more complex and requires more training and processing than what standard machine learning technique would allow. But when we say complex, we don’t mean things like space travel! Even something that might be super easy for humans, such as recognizing shapes and objects, is actually an extremely difficult task for machines. Let’s consider an example. Say you’re training a machine learning and a deep learning model to recognize a house. A machine learning model might recognize large, somewhat rectangular structures on the ground to be houses. A deep learning model, on the other hand, would recognize structures with windows, doors, and a roof to be houses. If you give both models a photo of a house that’s upside down, the machine learning model might not be able to deduce it’s a house because the ground and sky are not where it was expecting them to be, while the deep learning algorithm would see windows, doors, and roof and correctly conclude the object in the photo was indeed a house. What else can deep learning models do? Here are some more examples: Natural language processing : have you heard of ChatGPT? Languages are incredibly rich and complex, but deep learning models are able to consume and synthesize language like no other technique we’ve ever discovered. Autonomous driving : the future is here! Many car manufacturers (not just Tesla!) are now offering autonomous driving software. When conditions are right, you’re able to let go of the steering wheel and just let the car take you where you need to go. Autonomous driving models are only going to get better and we predict that every car will soon offer the feature as standard. Drug discovery : science is never easy and pharmaceutical companies are using deep learning models to analyst large chemical datasets to identify potential drug candidates and expedite the drug discovery process. How Do I Become an Expert? Becoming an expert in machine learning or deep learning requires a combination of experience, knowledge, curiosity, and skill. You certainly do not need to have a degree to be an expert, but schooling in the form of a bootcamp or academic courses definitely helps you to build a strong foundational knowledge to springboard your learning journey. Here are some steps we think you should take if you want to become a machine learning expert: Develop a strong mathematics foundation : machine learning is a branch of computer science and, as such, requires a strong mathematics foundation. A mathematics degree is not a prerequisite to pursuing machine learning, but you should be comfortable with topics such as linear algebra, calculus, probability, and statistics. If you’re not comfortable just yet, don’t worry—you can still get started on your machine learning journey and learn the theory as you go. Learn how to program : while machine learning is heavy on theory, it’s absolutely an applied field and the best way to practice machine learning or deep learning is with python. The machine learning community overwhelmingly uses Python and libraries such as Pandas, scikit-learn, TensorFlow, and PyTorch. Practice your skills : there is nothing more important than practice. There are many free, publicly-available datasets on which you can practice machine learning techniques. Hands-on experience helps you deepen your understanding and develop more problem-solving skills. Be active in the community : to be considered an expert, you have to always be up-to-date on the latest trends and findings. Connecting with professionals in the field, attending industry events, and reading academic papers are all ways to stay relevant. Pursue advanced topics : once you have a solid foundation, you can explore advanced topics such as deep learning, reinforcement learning, computer vision, or natural language processing. Focus on what you’re interested in. Which is Better: Machine Learning vs Deep Learning? We can’t say which one is better or worse because the relative strengths and weaknesses of each approach greatly depend on your specific problem set. In general, machine learning is a good choice for problems that can be solved with relatively simple algorithms while deep learning is a good choice for problems that require more complex analysis and can benefit from the power of neural networks. Let’s take a look at some cases where you might want to use machine learning but not deep learning, and vice-versa. Machine learning is better when… You have a small dataset : machine learning algorithms can train models with much less data than you would think. You need to interpret the results : deep learning models are often black boxes—if you need to understand the results of your model, you might be better with a machine learning approach. You need to make predictions quickly : it can be much faster to train machine learning models as they require less data and less processing power. Deep learning is better when… You have a large dataset : deep learning algorithms require vast amounts of data. You need to make accurate predictions : we’re not claiming that machine learning models are not accurate; however, if you need a precise prediction, you might be better with a deep learning model. You need to solve a complex problem : some traditional machine learning algorithms might struggle with finding patterns in complex datasets. Consider a deep learning model instead! In the end, the best choice of technique to use is dependent on the specific problem you are trying to solve. If you’re not sure about which approach would be best for your use/case, it would be a good idea to consult with a data scientist or a machine learning expert. With so many innovations in such a wide array of industries, machine learning is probably the most exciting field to be in right now. If you’re eager to be at the forefront of innovation, want to unravel the potential of data, and wish to make a significant impact on the world, then we think there’s no better starting point than enrolling in one of Ironhack’s bootcamps to get your start into tech. Embrace the journey, seize the opportunity, and empower yourself with the skills and knowledge it takes to break into the world of machine learning. What are you waiting for?

We’re sure you’re well aware of the recent rise of machine learning and are probably wondering what we can expect in the future. We’re not fortune tellers or particularly skilled at predicting the future, but thanks to the recent rise of machine learning technologies across almost every field, we have a pretty good idea of what to expect. But before we dive into the future of machine learning, let’s start with the past and present. What is it? Why has it recently become such a phenomenon? What does the past tell us about the future? What is Machine Learning? Part of artificial intelligence and computer science, machine learning uses data and algorithms to imitate the way humans learn and act , slowly improving over time. Within machine learning as a whole, we can separate it into four categories : supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning : machines receive labeled data to predict future outcomes and behavior; analysts review these results after to ensure that everything is correct. Examples include spam detection, speech recognition, medical diagnosis, and fraud detection. Unsupervised learning : machines receive unlabeled data sets without any output variable, meaning they’re able to detect hidden patterns or categorized ungrouped data. Examples include forecasting and predictive modeling, recommendation services, and anomaly detection. Semi-supervised learning: using both labeled and unlabeled data, machines can use both data sets to draw conclusions and sort information, improving accuracy and performance. Examples include medical image analysis, text classification, and fraud detection. Reinforcement learning : the most advanced method of machine learning is reinforcement learning, where the machine learns how to make adjustments based on positive and negative feedback and improves over time. Examples include personalized treatment plan development, autonomous vehicles, and games. Why is machine learning important? One of technology’s most important selling points is that it makes life easier for humans and that’s exactly what machine learning does. The ability to interpret large amounts of data and make logical decisions with minimal human intervention not only frees up lots of time that humans would have otherwise spent with that data, but also allows for incredibly large amounts of data to be processed quickly–a task that’s nearly impossible for humans. Another crucial factor is the speed at which both technology and customer preferences are evolving. In order to maximize profits and efficiency, companies need to keep up with what people want and this requires making sense of tons of data. When properly implemented, machine learning can cut costs, improve quality of life for both employees and customers, and eliminate risks. The origins of machine learning Machine learning first came to be in the 1940s (if you’re interested in the origins of artificial intelligence, which encompasses the beginning of machine learning, check out our post below!) but didn’t really come to fruition until the 1990s, when the world’s first spam filter was released. And while this invention was certainly a relief to those of us who were overwhelmed with high numbers of spam emails, it was an indication of something even bigger: a massive advancement in computer science and the birth of an entirely new field. Today, machine learning has taken over the world; some examples of machine learning include: Facial recognition Product/show recommendations Spam filters Text prediction autofill Social media optimization The Future of Machine Learning Now that we know what machine learning is and how it works, let’s dive into how it works and what we can expect to see in the future. There are four basic steps to machine learning: Choose your data : this is pretty obvious! Before you begin, make sure you know if you want to use labeled data (so that the machine delivers a certain outcome) or unlabeled data (you want the data to make autonomous decisions). Choose your algorithm : choose your algorithm depending on whether you use labeled or unlabeled data, the amount of data in the data set, and the desired result. Train your algorithm : choose the proper variables and parameters for your algorithm so that you get your desired results; this can be done completely by the computer, freeing up human time and energy. Improve the model : once the model has run a few times, check the output for any errors or issues and provide additional information or data to solve any existing problems. The future is clear: as data continues to play a massive role in our lives, machine learning will become even more important. We have so many examples of recent innovation in machine learning that we could spend all day telling you about them, but we know you’re not interested in that! Instead, we’ll highlight some advances that make machine learning’s future so bright. Personalization and recommendations One aspect where we can expect to see lots of progress is in the personalization and recommendation niche, where computers will get better and better at recognizing patterns, remembering user preferences, and understanding target markets. Just as Spotify always knows that perfect song to play next or Netflix the perfect show to recommend, this ability of machine learning enhances the user experience significantly. Chatbots Chatbots are great examples of machines that are constantly learning: they’re capable of remembering past conversations and experiences to adapt their answers and knowledge to meet customer needs. Not only does this free up humans to work on other projects where their brain power is needed, but also allows customers to find answers easily and quickly. Transportation You’ve heard of self-driving cars and thanks to machine learning, companies like Tesla have begun piloting the first models. With data from the surrounding vehicles and roads, autonomous vehicles are able to safely drive on their own, eliminating human error that can lead to accidents. Computer vision Imagine if a computer could look at an x-ray or blood test and accurately diagnose a patient. With machine learning’s advancements, we can expect a future where computers are able to instantly digest, understand, and identify images with extremely high accuracy. In addition to healthcare, this technology is also being developed for airport security. Does the future of machine learning sound promising to you? We agree! And if you’re interested in diving into this branch of tech, look no further. Ironhack’s bootcamps have just what you need.

When you think of data visualization, you may start thinking about numbers, graphs, and figures– yawn . Data can seem pretty boring sometimes, but most don’t acknowledge the importance of data and how it can influence the decisions that we all make; data is power and organizing it into an engaging and intuitive visualization is no easy task. Before we move into how to improve your data visualization skills , let’s dive into the basics of data visualization. What is Data Visualization? Data can be collected on almost anything: the number steps you walk in a day, the win-loss ratio of a football team, or the amount of time you spend on your social network apps. And the most efficient and impactful way to represent all this information is visually. The data presented will be not only impactfully and efficiently shown, but also intuitively. The main objective for data visualization is to take complex data and present it in an easy to understand and accessible way for a wide range of users; in doing this, data visualizations can reveal patterns, trends, and further insights more easily than staring at numbers, pure data or spreadsheets. Data visualization can be shown in a limitless number of representations since there are so many graphs, charts, maps, and infographic styles to choose from; your creativity is the only limiting factor. Some data visualization examples include: A line graph of the movement of stock prices from 1981 to now An infographic map chart of the world’s population A four-quadrant graph comparing ideas in terms of significance, duration, and emotions A density plot of the night price distribution of apartments in a city Data visualization can organize and summarize a massive amount of data that fits in one or two small graphs or infographics and the people that design them are data visualization specialists. Becoming a data visualization specialist Data visualization demands a person with an eye for art and a mathematically-oriented brain ; you need to learn not only about data analysis, programming, and mathematics, but also shapes, color palettes, image manipulation, and graphs. You may be thinking that it sounds like a lot to learn, but it’s possible to learn all of the necessary skills through odd jobs, data visualizations courses, and certifications, such as: An undergraduate and/or graduate degree in a subjects related to data analysis, computer science, data science, or mathematics A certification course A bootcamp in data analysis Online workshops to acquire specific skills For any profession, it’s important to develop a growth mindset and keep in mind that there is always room to learn and grow ; for data visualization specialists, software is mightier than the sword. Data visualization software What are some of the best data visualization tools that you need to learn? There are certainly soft skills that come in handy, but let’s focus on the technical and hard skills that data visualization specialists should master, such as: Tableau (best for interactive graphs and charts) Microsoft Power BI (best for business intelligence) Klipfolio (best for customizable options) Domo (best for original apps) Qlik Sense (best for AI) Visual.ly (best for simple projects) Learning how to use more than one of these data visualization tools can significantly help you in providing the most appropriate data visualization infographic for the client. Now that you have studied data visualization, know which software and tools to use to design and have experience at a job, what can you do to improve your data visualization skills? It takes determination, practice, and an open mind , but taking the following points in mind will definitely help. Study strong examples Data visualization infographics are floating around the internet along with businesses and organizations that present their information in an endless amount of charts, tables, and images. When learning how to improve your skills, it’s important to analyze and close the gap between your own data visualization results and others . You can learn not only from appreciating others’ work, but also by taking inspiration from other data visualization specialists. To go about finding those strong examples, you can do a simple Google search or check out the following websites: https://public.tableau.com/app/discover/viz-of-the-day https://infogram.com/examples https://informationisbeautiful.net/ Understand your data When you are presented with a massive amount of data, it can be difficult to sort through it and truly understand what’s looking at you straight in the eye. Nevertheless, when you do finally get through all the data, you may finally understand the meaning of all those numbers. If you want to create a more thorough and complete data visualization, try asking yourself the following questions at the end of your data analysis: What are the main patterns and trends observed in the data? What is the data distribution for each variable of interest? Are there any skewness, outliers, or unusual patterns that should be further investigated? How about surprising findings or insights? Are there any notable differences or patterns in the data when segmented by different subgroups or categories? How might these insights contribute to a more nuanced understanding of the data? Are the drawn conclusions shocking or expected? How might the analysis results be impacted by external factors, such as sample size, sampling bias, or non-response bias? Are there any steps that can be taken to mitigate these limitations? If you have already asked yourself these questions, a better understanding of your data may mean asking more personalized questions or simply consulting a trusted coworker for what they may see that you cannot. Simplify and declutter Thinking that they’re making the best possible data visualization infographic, perfectionists may add too much and end up designing busy or overly complex charts or graphs. One of the main goals of creating data visualizations is making the information easily digestible and accessible. You may want to impress people with a lot of colors and design elements; however, this normally distracts people from the fundamental objective of the chart: the data . If you want to make your data visualization chart refined and focused on the data, keep it simple ! Focus on design elements As we mentioned before, you don’t want to design a cluttered chart, but you do want to ensure that your design elements are aesthetically pleasing, high-quality, and intriguing. Choosing design elements provides the opportunity for data visualization specialists to finally use the right side of their brains and create art that catches the eye and incorporate color theory, theming, font, and powerful images. Trust your creative instincts and see where they take you. Become a storyteller What do humans like more than anything else? A powerful and emotionally provocative story with which they can connect. Once you have all your data analyzed, you have a plot to start with and then you can build on it. You’re the author now and by leveraging all the design elements at your disposal, you have the opportunity to create so much more. The following are questions that could be useful in creating your story: What is the main story or message I want to convey with this data visualization? Does the chart have a logical flow or narrative that guides the viewer through the story? Are the visual elements and design choices supporting the storytelling aspect of the chart? Does the chart effectively communicate the key insights or findings from the data? Are there any distractions or unnecessary elements that may detract from the story? Is the story clear and understandable for the audience? Creating a story can be a difficult task; however, using data to build that story is what will give people the opportunity to connect with not only the data before them, but also the message that it wants to communicate. Embrace the feedback What’s a big part of life that is both scary and extremely helpful? Feedback ! Exposing yourself to feedback can put you in an extremely vulnerable position and it’s completely understandable. Despite the fear it may cause, you can grow so much from listening to others give you their feedback. The most important tips to keep in mind when you receive feedback are: People have their own points of view that are shaped by their own experiences The purpose is to guide you where you need to make adjustments and continue on the same path It can be hard to not take feedback personally, but if you’re able to breathe through it and recognize those feelings aren’t meant to bring you down but rather raise you up, you can make huge improvements to your work. Sharpening your data visualization skills can be tough, but the aforementioned points may give you a necessary push in the right direction. It doesn’t matter how much experience you have, there are always new skills and perspectives from which you can learn ; that’s the most exciting part of fully embracing a growth mindset. Data-based decisions are a crucial part of the world and have the power to influence your peers, the executives at your company, or even politicians. If you would like to make an impact on the world with your data visualizations, check out our Data Analytics Bootcamp and become a data analyst.

When you think of hackers, you probably imagine someone with a mask in a dark room, plotting to overthrow major corporations, break into bank accounts, and generally control the internet. You know, Mr. Robot style. While this is definitely a more exciting way to see hacking, reality is a bit different: there are three types of hackers. There are black hat hackers , who infiltrate systems to plant malware and exploit system vulnerabilities for their own personal reasons, white-hat hackers, who seek out vulnerabilities in systems to fix them, and gray hat hackers , who may dabble a little in both. There are also a few other hats hackers can wear, which we’ll get into in this post. Ethical Hacking: What is it? White hat hackers are pretty much cyber superheroes, fighting cyber pirates and cyber attacks 24/7. They engage in ethical hacking , which is super important for the security of all the online systems we use in our everyday lives. How does ethical hacking work? Well, ethical hackers are hired to hack systems and figure out the holes that evil hackers (pirates trying to steal data or bring down a system) could use. They find flaws and fix possible weak points to prevent system break-ins. Think of it like trying to break into your own house before going on a long vacation. You’ll want to be sure that there aren’t any windows left open and that the doors are all locked. That’s basically what ethical hackers do for technology systems and that’s why they’re so valuable. Other types of ethical hackers The internet is a massive space, with many specific networks and systems. That means there are a number of paths you can follow if you’re interested in becoming an ethical hacker (remember, they are in high demand ! ). Blue-hat hackers are hired by organizations to bug-test new software system networks before they are released. They find the loopholes and weaknesses and fix them to keep criminals out. Red-hat hackers work for government agencies to test security systems. If you’ve ever had to login to pay your taxes or use a pin to access a government account, you know red-hat hackers have vetted that system intensely. They even use black hat tactics to take down cyber criminals. Both of these types of hacker fall under the umbrella of white hat but here’s the difference: blue-hat hackers work for private companies and red-hats are government contractors. If you want to be a white-hat hacker, the choice is yours: both paths are very lucrative. Rules to Ethical Hacking To stay white hat and not go gray, ethical hackers need to follow a few general guidelines. Let’s break it down: First, they must explain their plan to the organization or government they are working for and obtain complete authorization. Once they get started, they must report all security breaches or vulnerabilities found immediately. You can’t find a breach and then save it for yourself for later! Everything you find needs to stay confidential ; non-disclosure agreements are key. Finally, they have to leave the system cleaner than they found it, with no traces they were ever there. What kinds of things do ethical hackers look for? Like we said, the internet is a vast space, so the needs that each organization or governments have are totally different. As an ethical hacker, you’ll need to ask yourself a few questions: What is the security situation like? Why were you hired? What tech stack are the teams working with? Which programs and platforms are they using? What technology is in place? What about applications, operating systems, software, and more? Once you have a good understanding of the situation, you can start looking for vulnerabilities. In general, you’ll start by trying to get through any security solutions that are in place. Make sure you are hacking the most up-to-date version of any solution. Now it’s time to break into everything! There are so many different systems out there, the process can be complicated, and you may have to learn new skills to break systems that have upgraded their security and privacy systems. Or you may have to work with internal teams to understand where they perceive possible vulnerabilities and read up on past breaks. Ethical hacking means constant learning, both on and off the job. The Four Steps of Ethical Hacking There are four stages that ethical hackers go through when they do their daily work. Unsurprisingly, they are the same steps that black-hat hackers use, since they have to be on the same wavelength! To protect the organizations they are working for, white-hat hackers need to follow the same mindset that black hat hackers use. Research or reconnaissance The hacker needs to gather information about the target organization and identify areas that might be vulnerable. Here’s how that works: The first part of this phase is dumpster diving , which is when the hacker finds important information like old passwords that are stored in the system, identifies important employees within the networks, and gets a deep idea of how all the systems function. The hacker then engages in footprinting to gather data on the security situation and minimizes the scope of work by identifying the most important IP addresses and drawing a network map. This may involve mirroring a website and using search engines to research the organization and investigating current employees in case of impersonation. Scanning There are three methods to engage in scanning : pre-attack, port scanning or sniffing, and information extraction. During pre-attack, the hacker scans the network for details they found during the reconnaissance phase. The port scanning or sniffing phase involves using dialers, port scanners, and vulnerability scanners to gather data. Then the hacker collects information about ports, live machines, and operating system details to launch an attack. Obtaining access The hacker gains access to the system, applications, and networks and gains control of the systems. Then, they secure access and launch an attack targeting specific system vulnerabilities. Covering their tracks As mentioned before, the last step is covering their tracks . The hacker must conceal their access from system administrators by clearing the cache and cookies, adjusting log files, and closing open ports. The Value of Ethical Hacking As we’ve mentioned more than a few times, ethical hacking is an extremely lucrative profession . Companies invest millions of dollars per year to secure their systems from black and gray-hat hackers. Companies like Google, Meta, and Microsoft even hold events that are called “bug bounties” where they invite hackers to try and get into their systems. Hackers that find bugs and infiltrate systems are given rewards; governments and states have used the same tactic. It’s kind of a weird concept to pay people to break into your house. But that just shows you the value that companies with major budgets place on their security–sometimes they actually hire the hackers on the spot. How do you become an ethical hacker? Let’s start with the obvious: check your ethical track record. If you have a history of cybercrime, it’s not going to work well for you. Make sure your resume is squeaky clean and then get hacking! Here’s a quick overview of what you should do: Gain an understanding of both wireless and wired networks and learn operating systems like Windows and Linux Study firewalls and different filing systems and permissions Polish your coding skills Cultivate your creativity–you’ll need to be steps ahead of the bad guys and anticipate how they think! You’ll also need to consider what it is you want to achieve and why cybersecurity is the path you want to take in your professional technical development. Think about where your strengths are now and what aspects you will need to hone to achieve your goals and stay ahead of the bad guys. While a college degree may be helpful as you get into ethical hacking, this is a career that is going to require constant innovation . Short courses to plug the gaps in your current knowledge will always be important so that you are a few steps ahead of the black-hat hackers who are learning as they go. Invest in your professional development and you’ll stay competitive within the hacker pool. How do I get started?! A great way to get started is with a Cybersecurity Bootcamp ! Why? It will give you the skills you’ll need to land a job in the growing cybersecurity industry You’ll gain knowledge in one of the fastest growing areas in technology You will definitely get a massive return on your investment You’ll get access to an engaging community, in addition to super helpful career services and invitations to exclusive tech events that will catapult your cyber career Our bootcamps are either full or part-time, depending on your schedule, and you can choose to learn online or visit one of our amazing campuses. You will become a cyber superhero and work your creative muscles as you stay steps ahead of black-hat hackers to protect governments, institutions, and private citizens’ data and money. This is such an exciting career path to choose, and there is definitely a lack of trained professionals, meaning you are going to bring incredible value with your cybersecurity skills.

You’ve heard about it, but maybe you don’t know exactly what it is. Or you’re familiar with the concept, but want to know exactly what you can do with a career in machine learning. We’ll cover these doubts and much more in the article below. What is Machine Learning? Machine learning is a field of computer science devoted to training computers to learn and make predictions without being explicitly programmed to do so . Instead of relying on static instructions (think massive if-else trees), machine learning algorithms ingest, analyze, and interpret large amounts of training data to build models that can then be used to make predictions. Even if you’re just getting into machine learning, we’re sure that you’ve definitely heard about it before. Let’s demystify the world of machine learning by first answering some commonly asked questions. Is machine learning the same as artificial intelligence? The terms “machine learning” and “artificial intelligence” are sometimes used interchangeably, but they do not mean the same thing: AI is a field of computer science that encompasses the simulation of human intelligence in a large number of ways: natural language processing (ChatGPT), computer vision (Google Lens), and robotics (Boston Dynamics). Machine learning , on the other hand, is an approach to AI that focuses on algorithms that seek to enable computers both to learn from data and to improve their performance over time. Machine learning vs deep learning–are they the same? No, machine learning and deep learning are not the same thing! Deep learning is a subset of machine learning that focuses on creating models using multi-layered neural networks. Exactly what those are is not in the scope of this blog, but you can think of it as a really complex algorithm that requires a lot of data and computational resources to train. Do I need experience in web development to work in machine learning? No! Most people who work in machine learning have no experience in web development. Machine learning is all about developing and using algorithms to produce models that can make decisions and predict outcomes–apart from both web developers and machine learning engineers writing code, the two have very little in common. Should I study data science or machine learning? Well, it all depends on what you want to do! Data science is the application of statistical and scientific methods to gain insights from data---and you can definitely use machine learning algorithms to achieve such ends. Machine learning, on the other hand, is more about techniques that enable computers to make predictions. If you’re into practical applications, study data science; if you’re into theory, study machine learning. Now that we’ve defined machine learning and answered some of your burning questions, let’s dive a little deeper and explore how machine learning affects our daily lives. Real life applications of machine learning While machine learning is theoretical in nature, there are thousands of real, practical applications for machine learning models in use in industry today. We’ve already named some companies and products that use machine learning earlier in this post: OpenAI’s ChatGPT, Google Lens, and Boston Dynamics. But these companies and products are not outliers; machine learning techniques can be applied in all kinds of contexts, such as: Healthcare : here’s a real, human impact. Machine learning has been used for diagnosing patients, discovering new drugs, predicting patient outcomes, and improving treatment plans---helping millions of people. Finance : this is the pinnacle of big data! We’ve seen machine learning used for algorithmic trading, fraud detection, risk assessment, and (controversially) credit scoring. Transportation : this is where graph theory is in action. Machine learning algorithms thrive at solving problems surrounding route optimization, demand forecasting, and (you guessed it) autonomous driving. Agriculture : it’s not just about growing crops! While machine learning can help with crop yield prediction, important advances have been made in optimizing resource allocation, detecting diseases in plants and livestock, and even genetically engineering newer, better crops. City planning : Cities use machine learning to understand how cities will grow and execute proper city planning. Sales : lots of companies use machine learning for sales purposes; for example, car companies use machine learning to understand how buyers will behave in the future. Streaming services : music companies and streaming platforms build sophisticated models to personalize the customer experience to match desires and needs. Working in Machine Learning Have we convinced you that machine learning is the best path into tech for you?! That’s fantastic! It might seem like a complex field and, well, you’re right, it is! But there are lots of roles where a machine learning background is useful. Roles in machine learning Many people have preconceptions about what you can do with a machine learning degree or bootcamp under your belt. While machine learning is generally theoretical, the truth is that you’re not confined to doing research for the rest of your life. There are plenty of well-paying, practical roles that you can take on with machine learning in your toolbelt. Let’s go over a few of them: Machine Learning Engineer : bet you didn’t see this one coming! The word engineer implies practical and machine learning engineers develop and deploy machine learning models to do any number of cool and interesting tasks. Data Scientist : data scientists often do exploratory data analysis or hypothesis testing. Pattern recognition and machine learning go hand-in-hand; to that end, data scientists can use machine learning tools to analyze complex data sets, identify patterns, and then predict outcomes much faster than they would otherwise be able to. AI Architect : while still technical, architects also take on a quasi-managerial role. They design and develop the overall strategies and infrastructure for implementing machine learning solutions for an organization. AI Ethicist : here’s where we can find the intersection of machine learning and philosophy. AI ethicists are responsible for addressing issues of fairness, bias, and transparency in AI systems. And those aren’t the only roles available! Machine learning consultants, data engineers, and research scientists all use machine learning skills on a regular basis. In the future, we’ll see almost every role either using machine learning tools (like software engineers using github copilot) or creating tools with machine learning no matter your industry and that’s why machine learning skills are so highly-demanded by hiring managers worldwide. How to get into machine learning There are so many ways to break into machine learning. Different people have different preferred ways of learning, so here’s four ways we think you can gain a foundational knowledge of machine learning, or learn machine learning, if you will: Books : for those of us who learn best by reading, there are plenty of resources available. The two books we recommend for those just learning about machine learning are Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron and Machine Learning for Absolute Beginners by Oliver Theobald. Online Communities : as always, there’s a Reddit community for exactly what we need! Try joining and participating in r/MachineLearning and r/LearnMachineLearning . A quick online search can also put you in touch with like minded individuals who are starting out on their machine learning journey. Bootcamps : There’s no better way to learn than in a safe, structured environment. Ironhack offers exciting bootcamps for roles all over the tech landscape. Supercharge your learning and jump start your career with an Ironhack bootcamp! YouTube : deep in your learning journey and stuck on a specific problem? YouTube and other online videos can help you visually solve a problem or boast your knowledge and share solutions with the community. Is machine learning for me? Alright, we’ve captured your interest! We’re not surprised; machine learning is an incredibly interesting field that has practically limitless potential. What more could you ask for?! If you can answer yes to the following questions, getting into machine learning may be your next career step: Do you like learning about statistics, coding, and new technologies? Are you curious, creative, and want to find new ways to solve problems? Do you grasp new concepts quickly and are passionate about the work you do? Do you like data and want to use it to get answers? You answered yes to all of these questions, right?! We thought so! And you’re in luck: at Ironhack, we've designed our bootcamps so that your dreams can become a reality and you can become the next great machine learning professional. Check out our course offerings today and don’t hesitate! This is your time to shine.

Diversity and inclusion is constantly on the forefront of corporate discussions and there’s a clear reason why: it’s absolutely essential to business success . This fact has become widely accepted over past years and more and more companies are putting lots of time, money, and resources into researching diversity and inclusion. On a general level, companies that prioritize diversity and inclusion benefit from: Improved innovation and creativity : diversifying your workforce means that you’ll have employees from different cultures, backgrounds, education, and religions--and all of these people will bring their own experiences to the table , elevating perspectives that may have been ignored previously. Better results : with diverse voices at the table, companies will benefit from reaching diverse populations, expanding their customer base and reaching entirely new groups of users. A wider applicant pool : more and more candidates are prioritizing companies with both strong diversity and inclusion policies and a diverse workforce, meaning companies that are truly diverse will see more applicants. A better workplace environment: workers that feel safe, appreciated, and valued at work are proven to be better workers, reaching higher productivity and efficiency levels. Companies that highlight diversity and inclusion procedures are also less likely to spend resources on resolving issues that stem from a lack of diversity. Sounds great, right? While the aforementioned benefits seem awesome, it’s important to recognize the systemic issues from which a lack of diversity stems . Solving diversity issues isn’t as simple as hiring more women or people of color; it starts with identifying systemic issues and truly committing to resolving them: Throughout history, minorities and underrepresented communities have had less access to education, leading to a block towards jobs that require a certain level of education. And for those who work long hours or more than one job, taking time off to study can be an impossible task. Education is expensive and as we know that generally speaking, underrepresented communities have less access to educational funds, pursuing a lucrative field isn’t always an option. The infamous glass ceiling does exist, meaning that certain demographics are limited in their professional progress. This leads to a severe lack of diversity in leadership roles and less role models for these underrepresented people to see themselves in. Non-diverse workforces typically have more problems with inclusion, making inappropriate or even hostile jokes or comments at the expense of underrepresented groups. This leads to an unhealthy work environment and less probability that minorities who are there will continue to work there. These problems are systemic and rooted in years of racism, homophobia, and sexism, but diversity and inclusion efforts that are specifically designed to combat them can make a change. Diversity in Tech Unfortunately, the tech industry isn’t known for being the most diverse sector. In fact, here’s a few shocking statistics: 83% of tech executives are white The US Equal Employment Opportunity Commission reported that there are half as many African Americans and Hispanics in tech than in the private sector More than half of Google and Apple’s employees are white Despite making up more than half of the global workforce, women only make up 25% of tech workers and 11% of tech leadership roles 67% of European women in technology feel underpaid compared to their male counterparts 57% of women in tech , compared to 36% of men, feel work-related burnout Only 10% of women in tech work on a team with a female majority These statistics are shocking, we know. And while some of those systemic issues and figures may seem impossibly high and unchangeable, working towards increased diversity in tech is extremely important: The majority of tech companies work to improve an aspect of human life or meet a need; when only one group is represented at the table, the solutions tend to only meet that specific group’s needs. As more and more diverse experiences join the conversation, products will reach new audiences and help more people. Lots of tech solutions are the result of hours and hours of creativity and innovation. But a bunch of people with the same background and experiences will lack that differentiating factor and may not be able to harness the right amount of creativity to solve the problem. When we introduce diverse voices, however, new ideas flourish and familiarity is left behind. It’s hard to look at a leadership board or the voices of a company and see no one with whom you identify and then decide to get into that field. As we mentioned above, systemic issues are rooted in years and years of discrimination and take even more time to be undone; companies that start prioritizing diversity and inclusion now will elevate underrepresented groups that will serve as mentors, inspiration, and role models for younger generations and encourage that diversity to continue over time. There is one major way in which tech is becoming more inclusive for all: bootcamps . Tech Bootcamps Help Diversity Efforts If you re-read our list of systemic issues in tech, you’ll find one thing in common: a lack of opportunity for underrepresented groups , be it to physically attend class, be treated fairly at work, have role models, or afford education. And here’s where bootcamps come into play, along with skills-based hiring. What is a bootcamp? Bootcamps are short, carefully designed courses that have a specific goal in mind: preparing you to enter the workforce as a professional in your chosen field. Typically taking place over the course of a few weeks or months, bootcamps are offered in a wide range of subjects but have become increasingly prevalent in the tech sector, an area that is growing incredibly fast. Tech bootcamps focus on one specific area, such as UX/UI design, web development, cybersecurity, or data analysis, and teach students the exact tools they need to land an entry-level role. Many also offer career services to help propel students’ career changes, boasting interview practice, CV and portfolio review, and career development opportunities. The differentiating factor of bootcamps is, without a doubt, their ability to provide anyone the chance to switch careers and join the lucrative field of tech through being flexible, financially-accessible, diverse, and focusing on skills-based hiring. Bootcamps are flexible Lots of people are limited by their current situations and, in particular, women are unfairly tasked with the majority of household duties, childcare, and family responsibilities. Since traditional educational options are usually quite time-demanding, not flexible, and require years of study, this has severely limited the options of those who have other responsibilities. Bootcamps, however, typically offer lots of flexibility. Need to take the bootcamp remotely from your home so that you can watch your children? Or do you need to keep working your full-time job while taking your bootcamp? Bootcamps that offer part-time and remote options while still providing you with the same educational and career opportunities are a huge part of making this education more accessible to everyone. Bootcamps are constantly updated Tech moves so fast and is constantly requiring new skills, but that’s not the only thing that’s constantly changing. Bootcamps are known for teaching the most cutting-edge and up-to-date technologies and while this does mean that they’ll prepare you to enter the workforce, it also means that you’ll be right on top of addressing new issues. As the world and society as a whole places more of a focus on diversity and inclusion, the problems that minorities and underrepresented communities face are coming to the forefront. This means that when the next problem arises, bootcamp students will be right there, ready to find a solution and make the tech community a more accessible place. Bootcamps are financially accessible Traditional, four-year university courses are expensive. Even one year classes or online certifications can also come with a hefty price tag that makes it an impossible choice. Bootcamps, on the other hand, are typically much more affordable, offering scholarships, government grants, or financing options to those who need assistance. The ability to choose a part-time course and continue working while studying also allows you to keep your day job or other responsibilities while studying, reducing the financial burden. And since bootcamps are so set on preparing you to enter the workforce in the shortest possible time, many offer attractive financial options such as income share agreements, where you only begin paying off your bootcamp once you’ve landed a job that meets the minimum salary requirements. Bootcamps promote skills-based hiring Technology is advancing rapidly and it’s an unfortunate fact that traditional forms of education are typically outdated or take so long to complete that once you are done, there’s an entirely new set of tools and technologies that companies demand. To combat this increasing skills gap in tech, even the world’s top companies are turning to skills-based hiring practices. Instead of focusing on someone’s college degree or where they went to school, skills-based hiring looks at exactly that: the skills of the person . By eliminating the need for expensive and time-consuming degrees and instead making the skills a person has a priority, companies are both able to widen their applicant pool and accept underrepresented applications, in addition to hiring workers who have the skills they need. Bootcamps are diverse The three aforementioned points all help support one important point: bootcamps are more accessible, providing increased opportunities to underrepresented communities and minorities. And this is where the change in tech starts; as more and more underrepresented communities and minorities enter the tech sector, newer generations of future techies will see people who look and identify like them in their chosen field and in leadership roles, meaning they’ll be more likely to also follow that path. By being flexible about time and location requirements, in addition to giving students a fast and thorough education, bootcamps are on the forefront of change in tech, providing incredible opportunities to all. Are you ready to join the tech field and work to make it a more inclusive place? At Ironhack, we offer remote and part-time bootcamps, in addition to on-campus and full-time courses so that you can pick the best possible option for you. If you’re ready to get started, check out our course selection today and make your jump into the wonderful world of tech.

You’ve heard that math is universal, right? You probably assume that the same goes for technology, where numbers and code and specific design choices reign supreme. Or that no matter where you go to work in or learn tech, it’ll all be the same. And while that may be true if you’ve only worked in a few places, for the most part, the tech field is full of diverse experiences–and workers. But this isn’t unique to the tech industry! In fact, cultural diversity in the workplace is present in absolutely every sector. Before we dive into cultural diversity in tech specifically, let’s discuss five of the most common cultural differences that you’ll find in practically any workplace: Religion : lots of people’s values and thought systems are shaped by their religion and even more have grown up surrounded by people who practice the same faith. When confronted with people who have different ethical principles or lifestyles, religious differences can cause tension in the workplace. Ethnicity : different cultures have different ways of greeting people, communicating, or expressing needs and when multiple ethnicities come together, it’s quite easy for misunderstandings to take place. Sexual orientation and gender identity : LGBTQ+ folks are commonly subjected to biased jokes, invasive questions, or even harassment at work, due to their sexual orientation or gender identity. Education : both people’s educational experiences and where they went to university can lead to assumptions about their intelligence level; different teaching methods can also lead to contrasting opinions in the workplace. Generation : each generation has a specific way of working, combined with values that they’ve cultivated over time. When teams are made up of people from different generations, it can be tough to reach an agreement. Handling cultural differences in the workplace The five aforementioned cultural differences can cause quite the stir in the workplace if not properly addressed. Luckily, professionals have developed some tips that can help you handle any unfortunate situations: Create effective communication channels : the best way to avoid issues is to clearly communicate company policies and practices before any problems arise. Having team meetings where everyone expresses how they want to communicate and what their expectations are, as well as providing any new or visiting team members with a quick outline of company culture, can go a long way. Have reporting procedures : in the case of an issue, it’s crucial that your team feels safe reporting said problem to higher management or HR; plainly and clearly explain your company’s zero tolerance policy for any sort of discrimination or harassment and ensure that everyone is familiar with reporting procedures. Take the time to educate your employees : the vast majority of cultural difference problems arise from misunderstandings or a lack of understanding; if you give workers the space to explain their backgrounds and their customs, you may avoid future issues altogether. Sensitivity training : it may not sound super important, but sensitivity training helps workers understand how their co-workers think and act, taking into consideration their specific background and experience. Diversity and inclusion training : lots of diversity and inclusion problems come from a lack of education; training employees on cultural differences and how to deal with them can help avoid problems. If issues have already arisen, these training sessions can be a great place to address them. Cultural Diversity in Tech Just like in any industry, cultural diversity in tech is absolutely key. Why? Well, diverse people bring diverse experiences to the table and in an industry like tech, where innovations that help people are so key, diverse teams are absolutely crucial. Not totally convinced? Let’s check out some of the main reasons why cultural diversity in tech is essential: Companies with diverse employees can better understand the needs of diverse customers , therefore creating superior products and services that reach a larger audience. Diverse employees bring new ways of thinking and designing to the table , opening the team up to new possibilities. Companies that seek out diverse talent widen their applicant pool significantly, choosing from the best of the best, regardless of any cultural factors. While people are capable, of course, of creating products and services for folks outside of their race, gender, or religion, providing equal opportunities to all will allow for never before seen levels of innovation, creativity, and inclusion that will propel the business forward. Promoting tech diversity in the workplace If you’re convinced that improving your corporate cultural diversity is just what your company needs, we’re thrilled to hear it! To take that next step towards equality, here are some things you can do: Increase corporate flexibility : following the COVID-19 pandemic, remote work skyrocketed and many benefitted from working from home or from another location altogether. Not only will increased flexibility help you reach a larger candidate pool, but you’ll also reach more workers who might otherwise be unable to be in the office from 9-5 due to childcare responsibilities, family duties, or long commutes. As childcare duties fall mainly on the mother in many cultures, this could help broaden women’s access to tech. Take company culture seriously : writing a plan for diversity and inclusion in the workplace is great, but there needs to be guidelines in place for handling any issues that arise; consider also scheduling periodic check-ins with employees to work on any areas for improvement. Nurture diverse talent: women and minorities are historically underrepresented and underpaid in tech (and most workplaces!) and change begins with us. Take the time to foster diverse growth and ensure that all employees have equal access to raises, promotions, and other opportunities. Create diverse recruiting strategies: the aforementioned points will be useless if you don’t actually hire diverse professionals! If you’re seeing that your applicant pools are almost entirely white and male, it may be time to rethink your hiring processes. Try reaching out to organizations that focus on diverse sectors, like Lesbians Who Tech or Black Girls Who Code and really make an effort to include diverse candidates in your  hiring process. Have we convinced you of the need for cultural diversity in tech? We hope so; the change starts with each and every one of us. With a strong commitment to diversity and inclusion, we’ll be well on our way to a better and stronger tech sector. Are you ready to join? Check out one of our bootcamps today.

Does the infamous cloud seem like some sort of mystical being that you don’t fully understand? That’s totally fair–but, if you’re considering a career in tech, you should definitely be comfortable with describing the cloud and, of course, working with it! If you’re unsure what the cloud is or how it manifests itself in real life, look no further: this article has everything you need. What is the Cloud? Spoiler alert: it’s not actually in the sky, but it’s not a physical thing either. So what is it? Well, it’s a global network of servers that hosts a wide range of services , such as data storage, servers, databases, networking, and software over the internet. You probably have a few more questions, so let’s break it down: These services are maintained by a third-party provider who guarantees access through both public and private internet connections. The benefit of the cloud is simple: it takes away the responsibility of storing large amounts of data from individuals or companies, removing the need for personal and physical data centers. Users can choose exactly how much storage and access they need, constantly expanding or shrinking their cloud access. To put it simply, let’s try this: the cloud serves as your computer’s hard drive, but without limitations on data or slow processing times. By storing your data on the internet, it’s always available and there’s no limits to what you can do. This brings us to cloud computing, the on-demand delivery of computer resources over the internet. Four types of cloud computing There are lots of options when it comes to cloud computing, but we can group them into four areas : private clouds, public clouds, hybrid clouds, and multi-clouds. Private clouds Private clouds belong to a specific person or organization and are not run by a third party; managed private clouds provide IT hardware and digital infrastructure to those who use this model. Dedicated private clouds, on the other hand, are designed for the unique use of one user or company. These boast enhanced data security and easier recovery and system management; however, private clouds are more expensive to both create and maintain. Public clouds Public clouds are managed by a third party and offer services to practically anyone; these services are on-demand and scalable, meaning companies can select exactly what they need. Companies take advantage of public clouds like AWS, Azure, and Google because they don’t require setup and are easy to maintain, but risk less secure data and a limited ability to create exactly what they need. Hybrid clouds As the name suggests, hybrid clouds bring the benefits of private and public clouds together to give users a flexible yet scalable environment. This type of cloud offers better security and a more customized experience, in addition to a cheaper and quicker setup period. However, they require a detailed and complex IT environment, which is costly to maintain, and challenges when it comes to transferring data between public and private clouds. Multi-clouds Separate from hybrid clouds, multi-clouds work exclusively with public clouds, bringing together public clouds from various providers. By using multi-cloud services, users can take advantage of the benefits of diverse providers and avoid being negatively impacted if one provider has issues. Be careful, though, because security risks increase when more third parties are involved and working with different servers can complicate the efficiency of your projects. The benefits of cloud computing If the aforementioned advantages weren’t enough, don’t worry: we have lots more! The cloud is an incredible development that has helped lots of people transform the way they handle their data: Its pay-as-you-go model makes it an accessible option for all, regardless of the size of your company. Students, developers, and companies alike can access services like storage and computing without having to have their own servers. Users can work from any location and deliver their products to anyone, anywhere. Using the cloud is much cheaper than maintaining on-site servers. Today, the cloud offers pre-configured servers to help kickstart your product development. In fact, if you’re in tech, you’ve probably heard of the three C’s of cloud computing : cost convenience, and control. These are the three main reasons that people opt for the cloud: Cost : for both major companies and those learning how to code from their bedroom, the low cost of using the cloud is an absolute major plus. And for those working on their own, lots of services offer free options to help you get started, like AWS, Google Cloud, and Microsoft Azure. Convenience : what’s better than something that’s always right at your fingertips?! Nothing! The cloud is incredibly convenient, boasting pre-configured options to save you time, requiring smaller amounts of computer power, and offering options to users around the world. Control : depending on you and what you’re looking to achieve on the cloud, there are lots of available options for you, so that you're able to exercise the exact amount of control you desire: Infrastructure-as-a-Service (IaaS) : here, engineers are expected to build and manage the infrastructure to create the solution that will best support the company’s goals. Platform-as-a-Service (PaaS) : develop and deploy your application on the cloud and the cloud service provider will take care of capacity planning and software updates. Software-as-a-Service (SaaS) : don’t worry about underlying resources or services; just think about how you’ll use the software to achieve your objectives. Now that we’ve established just how the cloud works and what you can expect from it, let’s discuss some real life examples to show you just how important it is. Real Life Examples of Cloud Computing If you read our sister article about real life examples of artificial intelligence, you’re probably wondering how cloud computing manifests itself in your daily life like AI does. And while you might have been surprised by some of our artificial intelligence examples, we’re sure we’ll teach you something new today. Streaming services Many streaming services like Netflix need to provide streaming options to millions of customers in every corner of the globe. It would be nearly impossible to do so with physical servers, as they’d need to have lots scattered worldwide; this is why Netflix uses Amazon Web Services and content delivery networks (CDNs) to ensure that users can quickly and easily access content whenever they want. AWS handles Netflix’s storage, processing power, and data management requirements; Netflix has CDNs in various locations across the world to ensure all users have access. Healthcare Storing personal information in physical files was dangerous for a few reasons; on the one hand, accessing patient information was limited to those who had those files in-hand and two, this data could easily land in the wrong hands. By storing patient data in the cloud, healthcare professionals from different practices are capable of sharing patient history and ensuring that it’s safe from prying eyes. Slack Lots of companies opt for Slack for internal communications, using their user-friendly channels to break into groups, communicate to the entire company, or directly message another colleague. To ensure that communication is fast, even in the event of data center failure, Slack has multiple data centers and uses cloud computing for its scalability, reliability, and accessibility. No matter where you are or how many users are on the network, Slack guarantees fast and efficient communication through cloud computing. File sharing services The vast majority of users no longer save files on their computers; instead, they use services like Google Drive, Dropbox, and OneDrive to save their files on the cloud, freeing up space on their computer and ensuring easy access from anywhere. Airbnb You’ve definitely heard of Airbnb, the home sharing application that allows users to both list their homes as available for rent and search for accommodation in the area of their choice. Airbnb has absolutely massive amounts of data from all the property listings, booking information, customer preferences, help desk, and communications between users and would need large physical services almost everywhere to function correctly without the cloud. With the cloud, the application is capable of updating in real time, handling payments and providing customers with personalized recommendations. Messaging services Long gone are the days of phones storing SMS messages on the actual device; messaging services like WhatsApp and Facebook Messenger store your data and information on the cloud, allowing you to access it from pretty much anywhere. This is also highly beneficial in the case your phone or computer gets stolen; you can wipe your account information from your device remotely and then sign in on your new device and it’ll be like you never left. Ride sharing services Companies like Uber, Cabify, and Lyft also rely on cloud computing services to handle the immense amount of data they handle. From location tracking, payment processing, driver availability, rider requests, and fare calculations, they must work fast and efficiently to provide users--both drivers and riders--with what they need. One of the most beneficial uses of cloud computing here is its scalability ; depending on how many users are requesting services at a certain time, the cloud is able to meet that demand. Social media There are 500 million tweets per day--do you think Twitter has a giant data storage server where all those tweets are kept (and all those from the past?!). Of course not; all social media networks use the cloud and through this, are able to provide users with the ability to keep posting, creating, and accessing previous posts. The cloud is an incredible tool that’s helped us advance incredibly; there’s no need for large servers that can be unplugged or physically damaged. With the cloud, all our data is right there for us, exactly when we need it. Companies will continue to invest in cloud computing over the years, discovering everything they can achieve with increased cloud usage. Are you going to be there?

For those of us that have been raised in the era of the internet, basic aspects of our daily life may seem incredibly normal: contacting anyone anywhere in the world with a push of a button, telling Alexa to turn the lights on, or using predictive text to finish your text message. But these are all relatively new inventions that have seriously facilitated the way we live our lives, making it incredibly easier to do practically anything . And what are all these tools called? Artificial intelligence. Many think of robots and talking homes when mentioning artificial intelligence, or maybe even a ChatGPT-like tool that can do your job for you. And while these are forms of artificial intelligence and ones that have gained popularity in recent years, artificial intelligence is much more present than we might realize. In fact, you’d be hard-pressed to go even a few hours without using some form of artificial intelligence. But where did artificial intelligence come from? How did it gain so much power so quickly? Has it only been around for the last ten years? In this post, we’ll cover these questions and much more, explaining the origins of artificial intelligence, some key examples, and what we can expect from the future. The Origins of Artificial Intelligence Artificial intelligence isn’t something that’s new; in fact, even ancient civilizations were considering automated tasks to help lighten their workload: the wheel is a great example of an invention that took responsibility from people and moved it elsewhere. Humans were no longer expected to carry heavy weights for long distances; wagons were great ways to automate this task. Of course, however, the early history of automated tasks has nothing to do with the artificial intelligence we know today. To facilitate your understanding of artificial intelligence and its journey throughout the years, we’ve put together a timeline , separated into different stages, to show you its birth and progress. The beginnings of artificial intelligence: 1900-1950 As media portrayals of smart machines or robots entered the mind of the general population, researchers started trying to develop similar tools. Even though there was lots of development and progress throughout the years, artificial intelligence was just taking off and there was lots of room for improvement. 1900-1950: during this time period, lots of media reflected what would be known as artificial intelligence in the future, such as robots and objects that were able to think or act like humans. 1920s : the first known use of the word “robot” was by Czechian Karel Čapek and the first Japanese robot was built by Makoto Nishimura. 1949 : Edmund Callus Berkeley published a book that compared newer computers to human brains, exploring similarities. The first developments of artificial intelligence: 1950-1956 Now that artificial intelligence as a concept had been defined and was starting to gain usage, researchers began seeing what they could accomplish with the computers they had. 1950 : Alan Turing publishes “ Computer Machinery and Intelligence ” which would be used by experts to measure computer intelligence and opened the eyes of many to the possibilities of artificial intelligence. 1952 : Arthur Samuel teaches a program to play checkers, which became the first to play a game independently. 1955: The phrase “artificial intelligence” is used for the first time by John McCarthy at a workshop, kickstarting its worldwide usage. The progress of artificial intelligence: 1957-1974 More and more companies and institutions began dedicating time and money to artificial intelligence, looking for ways they could use these intelligent programs to make their lives easier. 1957-1974 : computers became more widely available and cheaper, in addition to faster and capable of storing more and more information. 1959 : the term “machine learning” was coined by Arthur Samuel 1961 : General Motors released their first robotic addition to the assembly line, taking over a job that was considered too dangerous for humans. 1965 : the first “expert system” was released, which was designed to mimic the thinking patterns and decision-making process of humans. The fast growth of artificial intelligence 1980-2000 With the rise of better technology and proven situations where artificial intelligence was beneficial, artificial intelligence took off and gained traction worldwide. 1980s : artificial intelligence regained some of its momentum as computers became increasingly skilled and capable of storing vast amounts of data. 1980s : the Japanese government funded expert systems development and artificial intelligence projects, investing $400 million in these technologies. 1997 : international chess superstar Gary Kasparov was beaten by IBM’s Deep Blue, one of the world’s first and simplest examples of an artificial intelligence machine that could function on its own. 1997 : Dragon Systems released the first speech recognition software for Windows, which was the first step towards language interpretation. Artificial intelligence today: 2000-present Artificial intelligence has continued to flourish in recent years, providing humans with easier ways to complete even the most basic of tasks. As computers become increasingly capable of handling large amounts of data and understanding how humans make decisions, artificial intelligence will continue to grow and flourish. Almost every tech organization has some sort of artificial intelligence tool in development and we’re eager to see what the future holds. Examples of Artificial Intelligence Artificial intelligence isn’t just robots that will take over the world (or your job!). In fact, the best examples of artificial intelligence are the ones we use daily and the ones we take for granted. Now that we know how artificial intelligence came to be, let’s dive into some examples of artificial intelligence in our daily lives and how it’s progressed over time. Text assistance We all know how cool it is to have your phone or computer predict what you’re going to write or type next, but text assistance has evolved over time; spelling and grammar revision used to be the only functions that text assistance artificial intelligence was capable of doing; today, however, our phones can use previous memory to accurately ( well , usually !) predict what we are going to type next. Driving assistance Remember the days of printing out directions and following those to your destination? Those days are long gone, thanks to artificial intelligence technology that’s able to use your current location and data from your surroundings to recommend the best route available, warn you about accidents, and adjust your route if any problems arise. But that’s not where artificial intelligence technology stopped when it comes to cars: companies like Tesla have released the first models of self-driving cars. This may seem like something of the future, but this technology uses data from other cars in the area to react and make adjustments. There’s still a lot of work to be done here, but rest assured: this is something to keep an eye on. Facial recognition Remember when you had to manually enter your password every time you wanted to open your phone or log into one of your accounts?! Maybe not, and that’s because the artificial intelligence technology of facial recognition has transformed our lives, making it easier to access our information securely. This technology has also advanced incredibly over recent years; what began as facial detection technology (the ability to detect that a human face was looking at the screen) quickly evolved into facial recognition technology (the ability to detect that you are looking at the screen). And an even deeper development was the change that came with the 2020 COVID-19 pandemic: the technology advanced to be able to recognize faces even with a mask on! We could go on and on because artificial intelligence is rampant and practically everywhere! But we don’t want to bore you too much; let’s move into the future of artificial intelligence and what we can expect in the coming years. The Future of Artificial Intelligence The future of artificial intelligence is practically limitless; we could go into lots of details about what we expect to see in the future, but we have a better way to explore the future of artificial intelligence; the different types of artificial intelligence can be sorted into four categories : reactive machine artificial intelligence, limited memory artificial intelligence, theory of mind artificial intelligence, and self-aware artificial intelligence. The first two are categories that fully exist in our world today: Reactive machine artificial intelligence : this type of artificial intelligence is the most straightforward and simple; it can react to user input and instructions, but is a bit limited: each task is treated as a new one and the reactive machine will react exactly the same to identical requests, as it is unable to use previous memories or experiences to make adjustments. Some examples include spam filters in your inbox or streaming service recommendations. Limited memory artificial intelligence : this type of artificial intelligence takes reactive machine tools a step further, using its limited memory and real-time data to learn from past experiences and make adjustments as needed. However, it is still limited to the exact task it’s programmed to do. Some examples include chatbots or self-driving cars. The other two kinds of artificial intelligence have yet to come into existence. The future of artificial intelligence lies in these two areas; artificial intelligence that can act within these two categories will truly transform our world. Theory of mind artificial intelligence: although it hasn’t hit the general public yet, theory of mind artificial intelligence is being actively developed and researched; when released, this type of artificial intelligence will be truly groundbreaking because it will be capable of understanding and responding to human emotions and thought processes. Self-aware artificial intelligence : last but not least, self-aware artificial intelligence will be able to not only understand and process the human emotions around it, but also have its own emotions and feelings. This type is far off, but one that will truly transform the field of artificial intelligence when released. The future of artificial intelligence is bright and has been since day one. If you’re interested in harnessing this up and coming technology to transform your career and be on the forefront of innovation, this is the field for you.