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In the vibrant realm of web development, fullstack developers have become highly sought-after professionals. Their ability to navigate both front-end and back-end technologies is revolutionizing the way websites are built. In this article, we’ll shed some light on the intricacies of fullstack development and provide a roadmap to entering the profession. But first, let’s define the role and responsibilities of a fullstack developer. What is Fullstack Development? Fullstack development refers to the practice of handling both the front-end (user side) and back-end (server-side) aspects of web application development. A fullstack developer is a professional who possesses the expertise to work on all layers of the technology stack . Their versatility and comprehensive understanding of the development process make them invaluable assets, bridging the gap between different technological domains . Let’s take a look at some of the specific responsibilities of a fullstack developer: Front-end development: fullstack developers are responsible for creating engaging and user-friendly interfaces that facilitate seamless user experiences. They employ HTML, CSS, and JavaScript along with modern frameworks such as React, Angular, or Vue.js to build responsive and interactive user interfaces. Back-end development: fullstack developers handle the server-side logic and architecture that powers web applications. They work with programming languages like Python, Ruby, PHP, or JavaScript (Node.js) to design and implement the server-side components. This includes employing DevOps practices to handle continuous server deployment and optimization. Database management: fullstack developers are proficient in database management systems like MySQL, PostgreSQL, MongoDB, or Redis. They design efficient database performance to enhance application speed and scalability. Integration and APIs: fullstack developers are adept at integrating various third-party services and APIs (application programming interfaces) into web applications. They leverage functionalities such as payment gateways, social media integrations, mapping services, and more. Testing and debugging: fullstack developers are responsible for testing and debugging applications to identify and fix issues across the entire technology stack. They perform unit testing, integration testing, and troubleshoot bugs to ensure application performance and security. Project management and collaboration: fullstack developers often take on project management roles, working alongside designers, product managers, and stakeholders. They understand project requirements, translate them into technical specifications, and ensure effective communication between teams. (BUTTON: Web Development Basics) Benefits of fullstack development Now that we’re clear on the responsibilities of a fullstack developer, let’s understand why their skills are so in-demand. Here are some advantages they bring to teams and organizations: Efficient development process: fullstack developers possess the ability to handle multiple tasks, reducing the need for extensive coordination between front-end and back-end teams. This results in faster development cycles and improved time-to-market. Seamless collaboration: fullstack developers can easily communicate with various team members, including designers, product managers, and clients. This increases accessibility and ensures a clear understanding of project requirements and goals. Cost-effective service: fullstack developers eliminate the need for hiring separate front-end and back-end developers, reducing overhead costs for companies, particularly startups and small businesses. Streamlined problem-solving: fullstack developers excel in troubleshooting as they have a holistic understanding of the entire application. They can identify issues across different layers of the technology stack and implement effective solutions. How to Become a Fullstack Developer So, you see the value that fullstack developers bring to the table and you’re wondering how to get started. Here are some practical steps to guide you on your journey towards becoming a fullstack developer: Establish a strong foundation in front-end technologies: an understanding of front-end development is crucial for fullstack developers. By mastering HTML, CSS, and JavaScript, you lay the groundwork for creating visually appealing and interactive user interfaces . Familiarity with responsive design principles and frameworks like React, Angular, and Vue.js enables you to build user-friendly applications that adapt seamlessly across different devices. Dive into back-end development: venturing into back-end development equips you with the ability to handle server-side logic and data management. Acquiring knowledge of programming languages such as Python, Ruby PHP, or Node.js allows you to develop the robust foundations of web applications. Familiarity with frameworks like Django, Ruby on Rails, or Express.js further enhances your ability to build streamlined and scalable server-side applications. Gain proficiency in databases: deepening your understanding of database management systems is essential. Mastering database design, complex query writing, and performance optimization enables you to ensure data integrity, increase application speed, and manage data effectively . Proficiency in popular databases like MySQL, PostgreSQL, MongoDB, or Redis empowers you to handle the storage and retrieval of data in your applications. Master the art of integration: developing your API integration skills is invaluable as a fullstack developer. By effectively melding external services (social media, mapping, payment gateways, etc.), you can enrich the functionality of your applications and elevate the user experience. Learn version control and collaboration tools: building familiarity with version control systems like Git helps you to easily manage and track changes within your codebase . Additionally, by utilizing platforms like GitHub or Bitbucket, you can collaborate with other developers; facilitate teamwork, share code, and enjoy a smoother workflow. Adopt DevOps practices: becoming competent in DevOps practices will benefit the reliability of your applications and bring greater value to your teams. Begin to investigate containerization technologies like Docker and explore cloud platforms such as AWS, Azure, or Google Cloud; understanding the process of continuous integration and delivery is essential knowledge for efficient development. Build a diverse portfolio: putting your skills into practice through real-world projects is key to becoming a successful fullstack developer. Create a portfolio that showcases your ability to create comprehensive solutions on both the front-end and back-end. This not only demonstrates your technical proficiency, but also highlights your creativity to potential employers or clients . Types of fullstack developer jobs Now that we’ve gone over the roadmap into fullstack development, it’s time to discuss an essential topic: employment! Fullstack developers enjoy a wide range of career possibilities, and depending on the type of job you’re looking for, they can look quite different. Let’s consider some options: Web development agency: working as a developer in this setting offers a large variety of projects and clients. You’ll have the opportunity to work on various web applications, from e-commerce platforms to content management systems and collaborate with multidisciplinary teams. The fast-paced nature of agency work allows you to continuously learn and apply new technologies. Startup or small business: joining a budding business environment, you’ll often have the chance to contribute significantly to the company’s web applications. With fewer resources and more autonomy, you’ll gain exposure to several aspects of the development process, making it an excellent opportunity for professional growth. Enterprise-level company: contributing to a large organization often means streamlining their development process and bridging the gap between teams. Being a part of large-scale projects with vast user bases and advanced technological requirements, you will have the chance to contribute to mission-critical applications. Freelance developer: providing you the flexibility to work on your own terms, freelance development allows you to choose projects that align with your interests and expertise. Fullstack developer remote jobs eliminate geographical barriers, enabling connection with teams worldwide. Working remotely not only provides the comfort of your preferred workspace, but also fosters cultural diversity and encourages collaboration on a global scale. Resources for fullstack developers Before setting you off on your path to become a fullstack developer, it is crucial that you have a collection of valuable resources at your fingertips. This list will serve as a north star, aiding in continuous learning, empowering you to overcome challenges, and unlocking new opportunities. Without any delay, let’s take a look at the following resources and how you can utilize them: Developer communities and forums: engage with platforms like Stack Overflow, GitHub, and Reddit. These sites provide opportunities to seek guidance, ask questions, and learn from experienced developers. Active participation in these communities can expand your knowledge and offer new solutions. Open-source projects: contribute to open source projects to gain experience working with other developers and sharpen your skills. Platforms like GitHub provide a large collection of open-source projects across various domains, allowing you to learn from real-world codebases and make important contributions to the community. Fullstack development conferences: attend conferences focused on fullstack development in order to network, learn, and stay updated on industry trends. Events like ContainerDays, Connect.Tech and NG-Conf bring together experts and enthusiasts from around the world, offering workshops, presentations, and panel discussions on several aspects of fullstack development. Certifications: consider obtaining a fullstack developer certification to validate your skills and enhance your credibility. Certifications like the Microsoft Azure Developer Associate, Google Professional Cloud Developer, or Amazon Web Services DevOps Engineer demonstrate your expertise in specific cloud platforms and their associated development services. WebDev blogs and podcasts: keep tabs on resources like Smashing Magazine, CSS-Tricks, A List Apart, and podcasts like Syntax and Full Stack Radio. These content hubs help you stay up to date on trends, best practices, and inspiring insights for your fullstack development career. So, as you venture into the dynamic field of fullstack development, remember that you have the power to shape the digital landscape and bring real value to organizations and users alike. Embrace the challenges, stay curious, and never stop learning. If you’re ready to fast-track your fullstack development career, you’re in the right place. Join an Ironhack bootcamp to refine your skills and elevate your impact. We can’t wait to see you in class!

As soon as we wake up in the morning, we use our five senses to take in all the information we can from our surroundings to interact with the world and make the most informed decisions. In the tech world, companies rely on data to guide and point them in the right direction. Data collection and analysis has become an essential part of a company’s arsenal and in the past few years, cloud computing has risen to power as an essential tool for not only companies, but also society as a whole. What are these tools and how do they provide companies with further benefits as they interact with one another? Let’s dive in. What is Big Data Analytics? There are two parts to focus on: big data and analytics . Analytics entails the evaluation and analysis of data from which companies can make insights, discover trends, and make decisions. Big data may seem just as obvious as analytics and in a way it is: it’s just a massive amount of data. But you may ask yourself what makes it big? Big data is about the five Vs: Volume: this is where the word big comes into play because volume refers to the vast amount of data with which these companies are working. Velocity: the speed that data collection and analysis takes is an important part of the process although it normally is expected to be near instantaneous with some big data analytics. Variety: data can be found in three different formats: structured (relational databases), semi-structured (XML, binary executables, HTML, EDI), and unstructured (images, business documents, emails, and websites). Value: when we are thinking about the value, we are answering the following question: what is the goal of collecting and analyzing all the data? The objective is to extract worthwhile insights, patterns, and trends , which provides the data with that value. Veracity: since the data is coming from a variety of sources, the reliability and accuracy of the data is extremely important which is why analysts have to ensure its quality before adding it to the list of data. When data analysts keep these five Vs in mind throughout the process, they are able to provide the best possible results from the most accurate data available. Big data analytics is leveraged in so many ways to help companies make the right choices and one of the most common ways in which we interact with it is through social networking . Big Data Analytics Examples If you have used social networking applications or websites, then you’ve definitely interacted with big data analytics in some shape or form. In recent years, the use of ads on these platforms has skyrocketed and because of this, companies have wanted customers to have the best possible experience when using their services. When a user is on a social network, there are several big data analytics functions happening at once. All of the actions you make on their pages like watching videos, clicking on or writing comments, liking videos or photos, inputting hashtags and much more can become influential data that is collected and analyzed to optimize user experience. Some of these include the following: Personalization: the data that social networks harvests go into databases that show a footprint of what would improve the user experience. The more personally intune to what the user likes, the more likely the user will return and interact more with the social network. Targeted advertisements: by knowing what a user likes, dislikes, and even what they may need, social networks can provide the right advertisement at the right time. If a user starts liking and consuming more content related to a hobby like boxing, wouldn’t it be the perfect time for them to buy products related to boxing ? And all of a sudden, boxing gloves, wraps, and boxing gyms show up as advertisements between friends’ posts. Suggested content: one like has a lot of power and can set off a chain reaction of content that a social network can then present to you. Recently, social networks have taken the data of a user to show them videos, photos, and posts that reflect back what they have already been searching for and connecting with on the network. To keep clients and customers on their applications and pages, social networks make a world that provides the users with hit after hit of serotonin and the best way to do that is use all of the data they collect, analyze it, and provide a personalized user experience . Benefits of Big Data Analytics As seen with social networking, big data analytics has many advantages that make it a powerful tool for companies to make clients come back again and again to their product. Some of these advantages of big data are: Personalized user experiences: as mentioned above, massive amounts of data go into data analytics to make the user experience as personalized as possible so that people feel the lure of the product; after all, humans like the familiar and comfortable. When new life events happen, data can provide fresh content that provides further opportunities for people to interact with more of the same thing. Improved productivity: one of the ways in which companies leverage data is to show inefficiencies and areas of improvement within their business. More accurate decision-making: data holds the key to unlocking the insights, trends, and patterns that will help companies make the most beneficial decisions for their future. More creative innovations: by providing companies with data on what does and doesn’t give them the attention they want, employees can use that feedback to create innovative and creative products and solutions to problems that they may have never thought of without it. Big Data Analytics Tools To run data analytics, data analysts rely on software to evaluate datasets and generate insights and trends and it is used to manage volume, velocity, and variety . Here are some of the tools that data analysts most often use: Apache Spark Tableau Apache Flink MongoDB Apache Storm Atlas.ti. HPCC These big data analytic tools have their own advantages that will help in different situations and data analysts rely on other types of technologies such as the cloud to complete their analyses. What is the Cloud? If you have a device that has access to the internet, it’s very likely that you have some sort of cloud service. Devices have a limited amount of physical storage space and to complement that physical storage, users can pay for more space that relies on access to the internet. There are physical servers elsewhere that house the data and the internet is the bridge that connects the device to the physical server. Some services that provide cloud storage are: Amazon Web Services Google Cloud Platform Microsoft Azure IBM Cloud Databricks Snowflake Qubole Whichever cloud or data storage service that a company pays for will be instrumental for them in improving and maximizing their big data analytics process. What’s the Role of the Cloud in Big Data Analytics? The cloud plays a vital role in analyzing big data, since it provides a large amount of storage space and data analysts accumulate and store a massive amount of data for all of the insights and trends they generate and discover. There are a variety of benefits when depending on cloud storage for big data analytics like: Cost efficiency: although companies must pay for cloud services, they are actually saving money in doing so since purchasing, setting up, and maintaining cloud infrastructure on-site can end up much more expensive. Housing physical servers also occupies a large amount of space that could be more efficiently used. Accessibility: using the cloud for data storage permits a user to access it as long as they have an internet connection; with the internet, you can be at home, in another country, or even on a plane and retrieve any data you need . Scalability: since data analysts are working with datasets that are constantly fluctuating in volume, the cloud provides a flexible amount of storage space that can always be reduced or increased easily. Real-time analytics: in addition to storage space, there are managed streaming services that complete instantaneous real-time data analysis. The services you can find include Amazon Kinesis, Azure Stream Analysis, and Google Cloud Dataflow. With the amount of data that devices, applications, and websites are compiling, storing and analyzing, companies are able to discover many insights and trends that will assist them in making the best decisions thanks to the help of the cloud. If you’re looking to work with data and dive deeper into the world of analytics, look no further than Ironhack’s Data Analytics course.

There are a lot of options out there for your cloud storage. Choosing the right provider isn’t an easy feat; after all, as your company begins receiving more and more data, you need a trustworthy and high-quality provider to not only store your data, but protect it. In this article, we’ll explain why you might need a cloud provider, dissect the three most common options (AWS, Azure, and GCP), and help make your decision a little bit easier. The Cloud: what is it?! We’re sure you’ve heard about the cloud. I mean, you would have to be living under a rock to have made it this long without hearing about it. But what is it? Well, the cloud is a collection of servers over the internet that host large amounts of software and infrastructure. In case that wasn’t completely clear, let’s make it even simpler: the cloud allows you to store your data on the internet instead of physically on your computer’s hard drive, freeing up lots of storage space and allowing you to work with very high quantities of data. Benefits of cloud computing Cloud computing’s popularity has skyrocketed in recent years, with more and more companies and individuals opting for this storage method. Here’s what the cloud promises: More flexibility Better performance Lower IT costs Improved innovation Sounds good, right?! Just wait--there’s more. Enhanced usability and accessibility A mysterious storage unit in the sky may seem like it’s above your paygrade–but it’s not. Cloud storage is incredibly easy to use and accessible to a wide range of users, allowing people from different places to access the information with ease. It also allows users to access their data from mobile or desktops–all they need is an internet connection. Improved security You’re probably thinking that storing all your data on the internet compromises your security; we get it–you want to protect your data and the idea of it being accessible to anyone isn’t appealing. But the cloud does boast incredible levels of security, in addition to the added protection of not being in a physical location–there’s no danger of a warehouse fire or provider collapse. Cloud security isn’t completely developed and will continue improving over time. But for now, training your team on proper cloud practices can help further your cloud security. Better team coordination Looking to share a file with a colleague located across the world? Or want to make certain information available to a specific team? The cloud allows you to easily share files (securely!) with your entire team and even limit permissions, based on your preferences. Multiple users can access the information from different locations, saving you time when it comes to collaboration. Applications of cloud computing Okay, you get why the cloud is a great resource, but how is it used in daily life? Is it something that all companies, even small ones, can benefit from? Absolutely: Online data storage : companies are collecting more and more data about their performance, client preferences, and overall company information and it’s simply impossible to expect them to maintain physical storage units with expensive hardware; this is where the cloud comes into play and offers a remote and digital place to store the data. Backup and recovery: if your data is held online, you can benefit from data redundancy and recovery plans, which might be storing a copy of your data on a different data center so that in the unlikely event of a data breach or outage, you can easily access your data. Big data analysis : as we mentioned above, companies have started receiving so much data. So much data, in fact, that they can’t realistically store nor process it. Cloud computing also allows for data processing and analysis, helping you automate an incredibly important and time-consuming task and allowing you to draw valuable conclusions from your data. So now that you’re totally convinced that cloud storage is the way to go, what’s the next step? Here’s a hint: finding the right provider for you. Cloud Providers Even though it’s on the internet, the cloud still has companies behind it, ensuring it works as desired and offering new benefits for clients. These providers are IT companies that enable the cloud to work as desired, powering the cloud’s computer power, data storage, applications, and more. There are lots of different options for the cloud, but three main providers stand out: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) . These different options encompass nearly 65% of all cloud services and work to offer clients a unique cloud experience that’s tailored to their needs. We’re going to dive into each of them, discuss their advantages and drawbacks, and leave you with a clear picture of the best choice for you. Ready? Amazon Web Services The cloud’s popularity has skyrocketed in recent years, but that doesn’t mean it hasn’t been around for longer–AWS was launched in 2002! You might think of instant delivery or streaming services when you heard about Amazon, but AWS actually makes up a giant part of Amazon’s profits. If you choose AWS, you can choose from servers, storage, networking, remote computing, email, mobile development, and security; AWS is the industry’s leading cloud provider , with nearly 33% of the cloud market monopolized. Benefits of Amazon Web Services AWS might be the right choice for you, depending on your specific needs. Here’s what you can expect: Using an operating system that clients are familiar with, AWS offers a user-friendly programming model, architecture, and database. AWS is cost effective, offering clients no upfront cost and no requirement to estimate usage. AWS is a very attractive option for smaller companies or individual users, given that they can use just what they need and pay for exactly that. Thanks to closely monitored and contained data centers located across the world, Amazon can simultaneously offer a secure and reliable service. As the industry’s preferred option, AWS is a great choice. However, keep in mind that additional support packages may cost extra and what you’re allowed to do in AWS depends heavily on your region and locations; if you have teams accessing your servers from different locations, you might face diverse restrictions. Microsoft Azure Microsoft Azure is another of the three main cloud provider options, offering clients storage, analytics, networking, and computing services. AWS is the most popular and largest cloud computing platform, but Azure is the fastest-growing and second largest , working hard to close the gap between AWS with every new innovation. And that’s not all–80% of Fortune 500 companies choose Azure for their cloud needs. Benefits of Microsoft Azure So why do users choose Azure if a larger and more popular option exists? Because: It’s fast , offering users the ability to access their data at practically any moment from basically every location across the world. In addition, its speed means that automated solutions are executed faster, saving valuable time and resources. It’s flexible , allowing users to easily switch between service levels to adjust to their current needs with no restrictions or minimum plans. It also permits all languages and frameworks, opening usage to a wider audience. It’s secure , prioritizing and publicizing their commitment to data safety and investing more than $1 billion yearly in security. Azure also boasts more compliance certificates than any other provider. It’s reliable , boasting some of the industry’s most complex and well-developed backs and virtual systems testing, placing their focus on the security of your data. Azure’s competitive advantages are worth considering, but keep in mind that Azure does demand expert management and monitoring, meaning you’ll need to either hire a professional or upskill a current employee to handle that responsibility. Similarly, a properly-working Azure demands platform leaders to be well-versed in the cloud and all services, not just a few. Google Cloud Platform Our last cloud provider is Google Cloud Provider, which runs on the same infrastructure that Google uses for other products, such as YouTube and Gmail, offering users computing, networking, big data processing, and machine learning and artificial intelligence services. Google joined the cloud party after Microsoft and AWS and doesn’t boast the same wide range of services just yet, but is working to improve their offerings. Benefits of Google Cloud Platform Despite being the last provider to enter the market, Google created the Kubernetes standard– a standard that AWS and Azure have since adopted . Here’s what else you can expect from GCP: Clients can expect a focus on big data, machine learning, and analytics, with a service designed for cloud-native business. It’s also known for incredibly fast response times. For those companies that work directly in competition with Amazon, GCP has become a great option and integrates cleanly with other Google services. Ensuring your website is always live, during security checks, updates, or outages, is a crucial part of e-commerce; Google offers live virtual machine migration, meaning GCP ensures your site is still active while migration is underway, maintaining your high performance. GCP, even as the newest provider, has the biggest global presence , offering users in 28 regions, 85 zones, 146 edge locations, and more than 200 countries access to the cloud. We hope this article was useful for you and helped you decide which is the best for your company or individual use. But if, on the other hand, you want to get to work on the other side of the street and work to develop better and more advanced cloud services , there’s a gigantic market for skilled cloud professionals. In fact, a bootcamp might just be the perfect way to get started. What do you think?

Artificial intelligence seems like it could change the world, right? It’s capable of writing the perfect party invitation, telling jokes, or anticipating what we want to see/say next. And while it can do all these things, it’s important to remember that artificial intelligence has certain limitations and should be used with caution, at least for now. What do we mean? Well, let’s go back to how artificial intelligence technologies are created. Data scientists feed computers and other machines with the information they want them to mimic , meaning this is the main source of information that machines have. Therefore, if there is any inconsistency or bias within the data, the computer will repeat that. If artificial intelligence is used for fun, such as writing a poem for a friend, there’s no issue. But when AI is used for decision making or is expected to draw conclusions on its own, bias, whether intentional or not, can severely impact the accuracy of the result. To understand exactly how ethics and bias play such a crucial role in artificial intelligence, let’s first cover the basics of AI and then give some examples of how bias and ethics can compromise the integrity of artificial intelligence. What is Artificial Intelligence? The ability of machines to replicate and mimic human responses and reactions to situations is what we know as artificial intelligence. By training machines to think like humans, we can automate otherwise tedious or repetitive tasks and use machine learning to process large amounts of data. Artificial intelligence has evolved considerably over time, but still has a long way to go to properly imitate human thinking. Even so, however, already existing advances have transformed the way in which we view machines and their potential. In our day-to-day lives, artificial intelligence manifests itself: In maps and transportation : ever wondered how your maps app can provide the latest information on traffic jams, closed roads, or the best route to take via public transportation, walking, or bike? Well, thanks to artificial intelligence, your maps app can update in real-time and provide you with the best possible experience. In facial recognition/identification : through collecting data about your facial structure and features, your phone is able to both recognize that there is a face in front of the screen and verify your identity. In writing assistance : spell check isn’t the only assistance you get from writing–thanks to the incredibly high amount of data that machines have been fed, they’re able to suggest what you can write next. Artificial intelligence is quite useful in a wide range of applications–that much is clear. But as with anything, there are concerns and limitations of which to be aware. Now that we’re clear about what artificial intelligence is, let’s dive right into ethics and bias in artificial intelligence. Bias in Artificial Intelligence A machine can’t have bias, right? After all, it doesn’t have experiences or memories from which to form said bias. Unfortunately, that’s not quite the case: machines can only learn from the data they have and if this data is biased, incomplete, or of poor quality, the output of the machine will reflect the same problems. The following are the most common examples of artificial intelligence bias: Algorithm bias : if the algorithm itself that determines the calculations of the machine are incorrect or faulty, the results will be as well. Sample bias : if the dataset you select doesn’t accurately represent the situation, your results will reflect this error. Example: you’re collecting salary information, but only record those of male employees. Prejudice bias: similarly to sample bias, prejudice bias uses data that is influenced by societal biases and therefore incorporates this prejudice into what should be opinion-free data. Example: you’re evaluating the gender distribution in certain occupations, but only count female teachers and male doctors, creating an inaccurate skew in your data. Measurement bias: measurement bias occurs when data is incorrectly gathered, specifically on how it was measured or valued. Example: if employees are surveyed about their feelings about their employer and promised a reward if enough employees answer, those who are motivated simply by the reward may not give thorough or accurate responses. Exclusion bias: you can’t pick and choose the data you use in your analysis and if you (intentionally or by mistake) exclude data points, your results will be inaccurate. Example: if you think the middle-of-the-road answers to a survey aren’t consequential and remove them, you’ll end up with data skewed to both ends of the spectrum and an inaccurate representation of how the respondents actually feel. Selection bias: while it can be quite challenging to get a big enough sample or one that’s representative of the entire population, choosing only certain groups can make your data completely useless. Example: you want to evaluate the universities that high school graduates choose to attend, but ignore those who choose to immediately enter the workforce or attend community college, therefore painting an inaccurate picture of your graduates’ choices. There are quite a few more ways that bias can appear in artificial intelligence, but the aforementioned ones are the most common. Here’s what you need to remember: artificial intelligence learns from the data that it’s fed and if that data is problematic or inaccurate, the outputs of artificial intelligence will be as well. Here’s what you can do to prevent bias: Lots of situations involving bias stem from small or limited datasets; do everything you can to collect as much data as possible from as many sources as you can, diversifying your dataset. As you begin to feed your computer with data, run tests during the early stages of testing to check for biases and correct them. Explore online fairness and bias tests to make sure you caught everything. Run your results by other experts to get other opinions and continuously check the quality of your data as time passes. Ethics in Artificial Intelligence You’ve definitely heard someone tell you that AI will take your job one day. And while the vast majority of jobs are safe (and those that AI can take over will morph into a different role), there are serious ethical considerations to keep in mind when discussing artificial intelligence. One thing is clear: the power of artificial intelligence is massive and we’ve only just begun to uncover what it can do. But the following considerations are absolutely crucial when it comes to maintaining proper ethics in the future of artificial intelligence: Privacy : we’re feeding machines tons of data about people to help it react in a more human-like way, right? How do we ensure that the data we’re giving to the machine is both secure and private? Prioritizing data privacy throughout the entire artificial intelligence lifecycle is one of the world’s main concerns. Human dependence : yes, artificial intelligence is capable of automating some tasks that humans were previously handling and it can also handle much more data than people can. But it’s absolutely essential that AI isn’t left to make decisions on its own, as it will never replace human responsibility and accountability. Sustainability : advances in artificial intelligence and technology are supported, but as long as they don’t come at the expense of the environment and overall sustainability. Accessibility : new developments should be accessible worldwide, not just in highly developed countries with easy access to technology. To ensure that ethics in artificial intelligence is prioritized, many countries and global organizations have come together to come up with policies and regulations, such as the GDPR in the European Union. But achieving truly ethical technological advances in artificial intelligence will come from a commitment from every individual, company, and country across the world. The power of artificial intelligence is truly unmatched–but it’s on us to properly use it for good. And skilled artificial intelligence professionals are sorely needed across the tech industry, so if you’re interested in entering this up-and-coming field, look no further: there’s lots of room for advancement in artificial intelligence.

The decision to go abroad at any point in your life is a major one, let alone choosing to go abroad for your career or education! The chance to spend time abroad is an incredible one that lots of people take advantage of when they’re studying in university or even after. But as a techie, you’re in a unique situation: tech is universal and it’s needed absolutely everywhere. Yes, everywhere! But heading abroad to work in tech isn’t the only way to use technology in your favor and gain a new experience: if you’re not in the tech industry yet or simply looking to upskill your existing tech knowledge, studying tech courses abroad is also a fantastic option. In this article, we’ll cover moving abroad for both studying and working, and, even though we’re sure you know them all, we’ll highlight some of the biggest benefits that working or studying abroad can bring you. Going Abroad: Why? You’ve heard about their experiences from your friends and family and maybe even had the chance to spend some time abroad yourself. So you know: time abroad helps you broaden your horizons , familiarizing yourself with new cultures and people. And you may even pick up some language skills along the way! Sounds nice, right? It is, but going abroad offers even more benefits: You’ll have the chance to not only get to know your chosen destination, but also its surroundings : with just a few exceptions, most cities are located near others that are maybe just too far to justify a trip right now. But if you’re already close by, it’s totally worth a quick trip, which can introduce you to yet another place, expanding your horizons even further. You’ll learn more : you’ve heard the stories about college students going abroad to party and while that definitely happens, we’re referring to your personal growth here. Going abroad can be a challenge at first and you’ll be faced with struggles you may not have faced previously: can you read another alphabet? Or search for an apartment on your own? New challenges mean more opportunities to learn– about yourself. You’ll expand your skill set : many go abroad to finetune their language skills, but that’s just one skill you can bring back from your time abroad. People from other cultures have different expectations about workplace behavior, learning methods, and practically everything else. Spending time abroad can make your resume even more attractive to potential employers. Although these reasons seem pretty great–and they are–let’s not forget that your situation depends heavily on your personal circumstances and if you’re heading abroad to study or work. Going Abroad to Work in Tech: Pros and Cons Maybe your employer has brought up the idea of you transferring to an office abroad. Or you’re just looking for a change of pace and are considering making that move abroad. No matter your situation, there are definitely pros and cons to consider. Pros of going abroad for tech jobs Let’s explore some of the great things that await you abroad in a tech role. New opportunities Depending on where you live, you could be limited in your career growth or general opportunities; other countries or regions could have job offers that fit more closely with your interests. You could also have the chance to learn new skills that aren’t commonly taught in your home country. Expanded network You never know when you’ll meet the next important person in your life and the same goes for the tech world. Working abroad can help you make connections that otherwise would’ve been impossible and help you build bridges across cultures (and maybe even oceans!) to expand your network. And who knows, maybe someone you meet while abroad might help you land a job in the future. Cons of going abroad for tech jobs Not everything is perfect, though, and we have to make sure you’re aware of some of the cons of working in tech abroad. Immigration challenges To combat high unemployment at home, lots of countries have rather strict immigration policies when it comes to foreign nationals working in their country. When you start thinking about working abroad, make sure you explore realistic visa opportunities and familiarize yourself with any possible limitations that might arise. Cultural differences What if your new country of residence has a completely different way of handling conflict? Or has strict guidelines on what is appropriate for the workplace and what isn’t? And if the new country is way more relaxed than yours, leading you to feel uncomfortable at work? Understanding and accepting cultural differences in the workplace is harder than it seems and can be quite the challenge at first. Going Abroad to Study Tech: Pros and Cons Your university might have a really cool study abroad program for future techies, or you’re looking for the best place to study a bootcamp and get into the tech field. No matter your situation, make sure you consider the following: Pros of going abroad to study tech If you’re considering heading abroad to study tech, you’ll benefit from an: Improved resume Whether you’re planning on staying for good in your new country or heading home once the travel bug has left your system, your resume will benefit from two major areas of working abroad: one, potential employers recognize the challenges that come from working abroad in a new culture and will value your experience. Second, you will learn invaluable skills such as cross-cultural communication, effective problem resolution, and teamwork, which will also help strengthen your resume. Expanded skill set If you choose to head abroad to a place where you’ve been studying the native language, you might be able to advance rapidly in your skills, adding something new to your resume. And even if foreign language skills aren’t your greatest strength, cultural experience and an openness to different cultures is something that looks fantastic on your resume. Cons of going abroad to study tech Complicated at-home requirements Lots of university programs have strict curriculum guidelines for their tech students, meaning that you may delay graduation by heading abroad. But if you’re totally set on the chance to study abroad, make sure you plan out your courses ahead of time to guarantee you’re still graduating on track. Tough adjustments Especially for those who have never been abroad before, the move might be all the change they need–no need for tough classes, new tech concepts, and a whole new style of learning. If you’re concerned about settling in while jumping into your tech curriculum, try to arrive before your program begins so that you can adjust at your own pace, without the pressure of classes immediately. We can sum it all up here: if you head abroad to either study or work in tech, it will definitely be worth it and make your resume stick out a bit more . But it will come with its challenges–being prepared for these difficulties can make your adjustment much easier. At Ironhack, we offer tech bootcamps in seven cities across the world, offering you lots of chances to improve your cultural skills and get into the awesome tech market. If this sounds like the right fit for you, what are you waiting for?! Send in your application today.

Cloud computing is becoming ever more relevant in the tech industry and among other sectors. In short, cloud computing is a model that utilizes the internet to deliver and access computing resources in a moment’s notice. As opposed to storing data and applications on local servers or personal devices, cloud computing enables individuals to take advantage of a vast network of servers hosted on the internet to store, organize, and manage data and applications. Cloud computing is a service that’s greatly expanding and can transform organizations and businesses if utilized correctly. That said, it’s no short of challenges for users . Learning how to overcome potential roadblocks is crucial in harnessing the potential of cloud computing. We're here to share a few tips and strategies on how to overcome potential challenges and transform your organization. Cloud Computing: A Quick Summary First off, what is cloud computing? We’ve all heard “it’s stored on the cloud...” but what exactly does that entail? Cloud computing is the online delivery of computing services . It utilizes remote servers, storage, and applications that are accessed through the internet to store data as opposed to saving such information on personal devices or local servers. Cloud computing enables users to access and take advantage of computing resources instantaneously because this information is saved among a vast network of remote online servers. The cloud service provider is responsible for the maintenance of the underlying cloud infrastructure, security, and management. The user, on the other hand, simply utilizes the services without stressing about the hardware and infrastructure management. Cloud computing services are becoming more and more popular today. They’re proving to offer immense advantages to organizations, including: Data storage and backup: in the most basic sense, cloud computing enables the storage and backup of important data. Data may be automatically replicated and backed up across multiple servers, ensuring protection against data loss. Flexibility and mobility: one huge advantage of cloud computing is the flexibility it provides to users. Instead of saving everything on one hard drive, users can store data online and thus access it from multiple devices, across borders, and without the stress of data being lost if one physical server breaks down. This allows organizations greater collaboration and reduces the risk of having all data stored in a single location. Security : security is a must for cloud computing because through storing everything online, organizations are vulnerable to cyber threats and data breaches. Those risks can be mitigated through proper encryption, access control, and regular security updates. On the contrary, through storing data in multiple locations, allowing for regular backups, and with secure data storage initiatives, data is in many ways safer than if it was simply stored on one personal device. Scalability : it’s easy to scale cloud services depending on organizational demands, giving businesses and individuals greater flexibility in determining their computing power and storage needs. Cost efficiency: ultimately, cloud computing services are cost effective because they rarely offer long term contracts and users can take advantage of the pay-as-you-go models which bill solely based on their actual usage. It makes the decision to try cloud storing services simple because it does not require a huge up front investment in infrastructure nor hardware. In short, cloud computing is an optimal, cost-effective way to secure a flexible and scalable solution for individuals and organizations looking to leverage computing resources, safely store data, and run applications without the stress of managing and maintaining a physical server. Applications of cloud computing Sure, we have an idea of what “storing something on the cloud,” means, but we often fail to see the numerous applications of cloud computing across industries and sectors. Cloud computing’s dynamic aspects foster the foundation for innovative services that go beyond simply storing data online . These services are particularly valuable for agile and DevOps teams . Here are a few common applications of cloud computing: Infrastructure as a service (IaaS): provides virtual computing resources like virtual machines, storage space, and networking capabilities. These virtual resources allow organizations to scale their infrastructure up or down with regard to consumer or business demand, without the commitment to physical servers and hardware. Software as a service (SaaS): applications delivered through the cloud that enable users to access and utilize online software applications. This reduces the need to install or maintain software or other equipment. Common examples of SaaS include basic email services, customer relationship management (CRM) softwares, office productivity suites, and other collaborative tools. Platform as a service (PaaS): as the name makes clear, PaaS provides a platform for developers to construct, test, manage, and deploy applications without the stress of managing the hardware or platform infrastructure. These platforms offer development tools, middleware, and access to databases which allows developers to concentrate on the development of applications, ultimately encouraging creativity and innovation. Function as a service (FaaS): also known as serverless computing, is a cloud computing model that provides a platform for developers to design, manage, and run applications. This reduces the work for developers to construct and maintain the infrastructure necessary to develop and release an application. Instead, through function as a service, cloud computing services provide cloud resources, carry out a block of code, deliver those results, and finally, destroy the resources such that they cannot be replicated. These are just a few of many real life examples of cloud computing services, and each encompasses a wide variety of programs, platforms, tools and softwares. Through these services, tech professionals have been able to transform everything from healthcare to transportations, to file sharing and messaging services . Challenges in Cloud Computing In order to reap the benefits of cloud computing services, tech professionals need to be aware of the potential challenges they may encounter . Here, we’ve outlined some of the most common roadblocks in cloud computing and offer tips as you navigate this ever growing online terrain. Security and privacy Of course security concerns are at the top of the list for challenges as it relates to cloud computing. Storing everything online on remote servers that are managed and maintained by external organizations should spark concern regarding data breaches, unauthorized access, and data privacy. Learning to implement robust security measures is the task at hand for data scientists and cybersecurity professionals. Ironhack’s tip: organizations must be proactive when it comes to monitoring security threats, encrypting data, implementing access controls, and leading regular security audits. Data analysts and cybersecurity professionals need to be up-to-date on current securitization practices and proactive in addressing potential threats. These risks will not disappear especially as the digital terrain is only expanding, thus, implementing robust security measures is crucial in mitigating these threats. Governance and compliance As the digital terrain expands, legislation is constantly changing and adapting in order to promote data security and privacy. That said, organizations must be in compliance with sometimes complex regulations such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Failing to be in compliance with said regulations could lead to legal consequences, financial penalties, and threaten your organization’s reputation. Ironhack’s tip: this work can be complex, especially when navigating sensitive data or international data transfers. Knowing that, organizations should establish robust data protection and privacy protocols, implement appropriate security measures, and regularly review and update organizational policies and procedures. Equally, invest in staff training to understand regulations and work to develop a company culture of compliance. Data transfer and bandwidth costs Organizations must be aware of the costs associated with transferring large volumes of data to and from the cloud. Often there are additional costs for transferring across regions and if poorly managed, organizations may face unexpected costs. Ironhack’s tip: in order to avoid overspending and optimize costs, organizations must monitor and manage cloud resources effectively. This means devoting time to planning and managing data transfers in order to optimize costs. Lack of cloud expertise Cloud computing services are relatively new and without doubt, they’re continually evolving and transforming. With that, many organizations lack experience with cloud services. That skills gap may lead to missed opportunities, poor decisions, or a failure to take advantage of all that these technologies can offer. Ironhack’s tip: organizations should collaborate with cloud service providers in order to better understand the tools and utilize the services to their fullest. Equally, it may be worth investing in training and upskilling workshops for employees such that everyone is up-to-date on the latest trends and best practices. Button: Upskilling/reskilling Complexity and management Maintaining and managing cloud resources is often complex, especially when folks have minimal training, the services are constantly developing, and the goal of contracting cloud computing services is often to not stress about their management. Furthermore, these resources become more complex in multi-cloud and hybrid cloud environments. Ironhack’s tip: in navigating these resources, organizations must invest in proper management tools and train their employees such that they possess the skills to efficiently monitor, coordinate, and optimize their cloud resources. In all, cloud computing is constantly developing and advancing in the services and applications it provides. While cloud computing can greatly support an organization through providing flexibility, opportunities for collaboration, security, and cost efficiency , organizations must be proactive in addressing potential challenges that may arise . Knowing those challenges and taking proper security measures, proactively monitoring work, and embracing continual improvement practices, businesses can effectively mitigate risks and overcome challenges in cloud computing. If you’re interested in helping your organization leverage the benefits of cloud computing , or simply are interested in learning more about data analysis, storage, and cybersecurity, check out the courses that Ironhack offers.

There’s no doubt that artificial intelligence (AI) is the future of online learning and data analysis: it’s slowly being integrated into every aspect of our lives. Services or tasks that previously had to be carried out by human intelligence can now be performed utilizing machine learning and computer systems. If done right, AI provides the opportunity to optimize tasks, improve organizational decision making processes, and enable creative problem solving. Staying up-to-date with AI systems may be met with unexpected hurdles ; that said, knowing how to confront these challenges head on is necessary for fostering strong businesses. Artificial Intelligence: a Quick Summary Artificial intelligence (AI) refers to the development of computer systems and machines that perform tasks that previously required human intelligence . This is done through the creation of intelligent agents that learn their environment, use reason, and make decisions to achieve specific goals. AI encompasses numerous subfields and techniques, including machine learning, natural language processing, computer vision, robotics, and expert systems . It’s quickly proving to have monumental impacts on numerous industries including healthcare, finance, transportation, and entertainment. The history of AI Despite being a term that’s in greater circulation today, artificial intelligence is nothing new. It’s a science that’s been around for decades , dating back to the 1900s with the early introduction of robots and other machines that modeled human behavior. In 1949, Edmund Callus Berkeley published a book titled, “Giant Brains. Or Machines That Think” that analyzed the similarities between emerging computers and human brains. This research and investment in developing smart computers expanded in the 1950s. In 1955, John McCarthy coined the term “artificial intelligence” at a workshop, later stating that AI "is the science and engineering of making intelligent machines , especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." Since the 1950s, AI has only expanded. Especially with computers becoming more accessible, the potential for artificial intelligence to transform our daily tasks, streamline workflows, and optimize our computer systems continues to flourish. At this point in time, almost every tech company has embraced AI in one way or another and AI systems are continuously advancing. Applications of artificial intelligence Today our lives are totally intertwined with artificial intelligence . Whether it be Alexa turning on our lights, the predictive text features that help complete our sentences, or our increasing reliance on chatbots like Chat GPT to accelerate research and support users in everything from writing cover letters to break up messages, we’re no stranger to the ways AI mediates our lives. Artificial intelligence has proved useful across industries, here are a few notable applications: Cybersecurity: today, cybersecurity professionals utilize AI to detect and prevent cyber threats through analyzing network traffic, noting patterns, and identifying suspicious activities. Health care : artificial intelligence is used in medical imaging for analysis and diagnosis and can help identify abnormalities in X-rays, MRIs, and CT scans. Similarly, AI has proven useful in drug discovery, genomics research, and personalized medicine. Finance : individuals who work in finance utilize AI for fraud detection, in assessing risk, and for algorithmic trading in the financial sector. Equally, chatbots have proven useful in servicing customers, answering questions, and providing automated investment recommendations. Transportation : AI is being increasingly integrated into automobiles through the enhancement of self-driving cars, as well as the introduction of computer vision, sensor fusion, and other decision-making algorithms that aid drivers. AI has also helped to optimize traffic flow, predict maintenance needs, and support the management of supply chains. Education : we hear both the stresses and the benefits that AI is having on education systems globally; of course there’s fear of students turning in AI-generated papers, but that does not mean these tools should not be utilized in educational settings. AI is utilized to personalize learning, facilitates adaptive tutoring systems, and can create intelligent assessment tools. This helps foster a more personalized learning path for individuals, allows one to target specific weaknesses, offers real-time feedback, and provides data-driven feedback to teachers. These are only a few examples of the applications of AI in our daily lives and the technology is only advancing. On one hand, AI is positively transforming lives and transforming the way we work from tech professionals, medical providers, and students alike. On the other hand, we must be aware of the challenges and potential for misinformation on the part of AI as we utilize these tools . The development of AI has sparked debate surrounding ethical concerns, privacy bias, and the impact that these chatbots will have on our job markets and society. Challenges in Artificial Intelligence Artificial intelligence, like all fields of tech, is constantly developing and transforming in ways that can lead to challenges for every sector. The toughest challenges to overcome with artificial intelligence are the following. Cybersecurity challenges Cybersecurity challenges in AI stem from preexisting vulnerabilities associated with data and AI systems. This could lead to attacks on AI systems, compromising the quality of the information received through AI systems, and risking the privacy and integrity of personal data . Or at another extreme, individuals may deploy AI for malicious purposes like the generation of deep fake content or automating cyberattacks. In short, securing AI systems is crucial in securing reliable information, making strong data-informed decisions, and ensuring the protection and integrity of data. Ironhack’s tip: data security professionals must collaborate with cybersecurity professionals in order to create and protect AI systems. In order to do so effectively, tech professionals may need additional adversarial training, workshops in secure data handling, and expanded information on privacy preservation. This work must be ongoing and tech professionals must stay up-to-date on trends in data security and potential threats to AI systems . Digital pathology challenges Digital pathology refers to the digitization and analysis of pathology slides utilizing AI . As opposed to viewing pathology slides through a microscope, these slides are digitized and converted into high-resolution images which allows pathologists to analyze and interpret the tissue in hand with computer-based tools. Despite revolutionizing this area of research, it’s a domain that faces challenges in everything from image quality standardization, annotation reliability, computational resource requirements, interpretability for pathologists, regulatory considerations, and workflow integration. Ironhack’s tip: in order to ensure high quality images, accurate annotations, and efficiency in computational processes, tech professionals must adhere to regulatory frameworks, integrate clinical workflows, and be ready to address ethical concerns. Strong collaboration between stakeholders is necessary in order to successfully confront challenges and realize the full potential of AI in digital pathology. Insufficient or low quality data AI systems function are trained on a set of relevant data in order to learn patterns, identify trends, and create predictive models that aid decision making. If that data is limited, biased, or inaccurate, the AI system may not offer reliable recommendations. It’s a great challenge given that data is sometimes limited, hard to access, or simply does not yet exist. Ironhack’s tip: Data scientists must work to find high quality, accurate, and representative data. They must control for bias and modify the work according to outcomes to ensure the integrity of the recommendations and control for biases and discrimination. Outdated infrastructure In order for AI systems to provide accurate and relevant recommendations and results, they need to process immense amounts of information in fractions of a second. That can only happen if the devices on which the AI systems operate are up-to-date, secure, and capable of providing the desired insights. Ironhack’s tip: if an organization is looking to utilize AI systems, they must be up-to-date on the latest tech trends, invest in infrastructure, tools, and applications that enable AI systems, and ensure that their tech professionals also possess the necessary skills and expertise to utilize these services. If the infrastructure is outdated, businesses are more vulnerable to some security threats and simply cannot secure the advantage that other organizations will secure. Ethical and legal implications As AI is widely adopted, we see the rise of ethical concerns ranging from integrity of work, intellectual property concerns, privacy, security, and potential impacts on job security. Ensuring that AI is deployed in a responsible and ethical manner is a huge challenge for many organizations. Ironhack’s tip: in adapting to AI structures, make privacy and security a priority. Collaborate with cybersecurity professionals to ensure that data is protected and the information is properly secured. Stay up-to-date with legislation surrounding machine learning, data privacy, and AI generated content and establish your own organizational regulations, guidelines, and frameworks that ensure fairness, accountability, and transparency. In all, AI has the potential to transform our world through improving efficiency, accuracy, and consistency in all aspects of research and data analysis. However, there are endless challenges for those of us looking to utilize AI systems. In order to successfully confront these challenges, organizations must embrace collaboration, interdisciplinary research, and stay clued into the ongoing work on academics, policymakers, and industry trends and developments. If you’re interested in learning more about AI, data analysis, and machine learning, then look no further. Ironhack offers bootcamps in data analysis and cybersecurity that will help you hone your skills and expand your knowledge as a tech professional.

It goes without saying that machine learning has revolutionized numerous industries and led incredible technological advancements. Whether it’s in healthcare or finance, machine learning algorithms have proven to be powerful tools for extracting insights and making spot-on predictions from massive datasets. But, just like any other field, machine learning comes with its fair share of challenges. Before we dive into some of the key hurdles of machine learning, let’s define what it is and how it’s utilized. Machine Learning: a Brief Summary Machine learning is a subset of artificial intelligence that enables computers to learn and improve without being explicitly programmed. Through the process of development and deployment, machine learning models can analyze and interpret data, discover patterns, and make informed decisions in real time . By leveraging statistical techniques and computational power, machine learning algorithms can continuously learn from data and refine their performance over time. Let’s consider a few of the benefits of implementing machine learning: Enhances decision-making: machine learning empowers businesses with the ability to make data-driven decisions. By analyzing large and complex datasets, machine learning models can identify the trends that humans might miss, enabling companies to gain valuable insights that can lead to improved efficiency and strategic planning. Tailors user experiences: machine learning employs algorithms that can analyze user data, such as browsing history, purchase behavior, and preferences. This enables companies to deliver targeted marketing campaigns, personalized product suggestions, and customized services. Automates tasks: machine learning automates repetitive and time-consuming tasks, freeing up human resources for more complex and creative endeavors. From chatbots and virtual assistants to intelligent process automation, machine learning algorithms work to streamline operations and increase productivity. Detects fraud: machine learning excels in identifying unusual activity and potential risks. By analyzing historical data and real-time transactions, machine learning algorithms can detect fraud, prevent financial losses, and improve overall system security. Applications of machine learning It’s clear that machine learning is a multi-industry game-changer, utilizing data-driven algorithms to transform how organizations can offer value . Let’s delve into some industry-specific applications of machine learning to gain a deeper understanding of its impact. Healthcare: by wielding machine learning algorithms, medical images like X-rays, CT scans, and MRIs can be compared to thousands of other images, helping radiologists make accurate interpretations and detect abnormalities. Additionally, factors like patient medical records and genetic history can be used to tailor specific treatment plans. Finance: by analyzing the combination of market data, historical trends, and news sentiment, machine learning models can develop an informed trading perspective. Combined with continuous integration, models help institutions enhance portfolio performance and investment strategies in real time. Retail and e-commerce: by providing personalized shopping experiences, optimizing supply chains, and predicting future demand, machine learning algorithms are reshaping the retail landscape. Models can examine market dynamics, competitor pricing, and customer behavior to maximize profitability. Transportation: by evaluating traffic patterns, optimizing routes, and making moment to moment decisions, machine learning is revolutionizing the transportation industry. From operating autonomous vehicles to predicting maintenance needs, machine learning contributes to making transportation safer and more efficient. Challenges in Machine Learning There’s no doubt that machine learning offers the potential for limitless innovation. However, behind the scenes, there are numerous obstacles that practitioners face on their path to successful implementation. Let’s outline some of the common challenges in machine learning projects and some practical tips to overcome them. Data quality: the foundation of success We can’t emphasize enough the importance of high-quality data in machine learning projects. Garbage in, garbage out, as they say. Poor data quality can lead to inaccurate models and unreliable predictions. Ironhack’s tip: invest time and effort in data preprocessing, including cleaning, normalization, and handling missing values. Perform exploratory data analysis to understand the data and identify potential issues. To verify data accuracy and completeness, collaborate closely with domain experts. Insufficient data and overfitting: the double trouble In many scenarios, obtaining a sufficient amount of labeled data can be a real challenge, especially in niche domains. Limited data can lead to overfitting, where models perform well on the training data, but fail to account for unseen examples. Consider these solutions: Ironhack’s tip: explore data augmentation techniques to artificially increase the dataset’s size. Utilize transfer learning by leveraging pre-trained models and fine-tuning them on your specific task. To make the most of limited labeled data, consider semi-supervised learning or active learning approaches. Model selection: navigating the sea of algorithms The vast landscape of machine learning algorithms can be overwhelming, leaving practitioners scratching their heads when it comes to choosing the right model for their specific problem. Ironhack’s tip: understand the characteristics and assumptions of different algorithms. Experiment with a variety of models and compare their performance on validation datasets. If needed, start with simpler models and gradually increase complexity, considering the trade-off between interpretability and performance. Interpretability: shedding light on the black box A common concern with machine learning models, particularly deep neural networks, is their lack of interpretability. The ability to explain the model’s decisions and predictions is crucial for reliability and accessibility. To tackle this challenge: Ironhack’s tip: exercise model-agnostic interpretability techniques, such as LIME or SHAP, which provide post hoc explanations for predictions. When being able to clearly articulate the process is a priority, consider using more interpretable models like decision trees or linear models. Depending on the industry, develop domain-specific frameworks that provide context-specific insights into the decision making process. Deployment and scalability: from prototype to production Building a successful machine learning model is just the first step. Actually deploying it in a production environment and ensuring scalability can be challenging. Here’s what you can do: Ironhack’s tip: employ cloud-based machine learning platforms that provide scalable infrastructure on-demand. To reduce computational requirements, optimize your model’s architecture and algorithms. Implement efficient data pipelines and monitor your system’s performance to ensure scalability and reliability. Continuous learning for machine learning practitioners Machine learning is an ever-evolving field and keeping up with the latest advancements is crucial to stay competitive. Consider nurturing your skills through the following resources: Read research papers and journals: familiarize yourself with high-quality research around various facets of data science. Publications like Transactions on Pattern Analysis and Machine Intelligence offer peer-reviewed papers on machine learning and computer vision. Engage in online platforms: explore sites like Kaggle, who offer machine learning challenges online. Also, Reddit and forums like r/MachineLearning and Cross Validated are communities that offer information and assistance in common machine learning challenges for beginners. Stay updated with blogs and podcasts: listen to podcasts like Data Skeptic, whose focus is around machine learning and artificial intelligence. Additionally, blogs like Machine Learning Mastery and Towards Data Science offer practical tutorials and share numerous resources. Attend conferences and workshops: consider taking part in conferences such as the International Conference on Machine Learning (ICML) or Conference on Computer Vision and Pattern Recognition (CVPR). Attending live events allows you to gain insights from industry experts and expand your network. Consider further education: improve your data science skills by pursuing further education in the field. Bootcamps through platforms like Ironhack provide comprehensive training in data analysis. Well, we think we’ve covered it! Mastering machine learning is no easy feat, but armed with the right knowledge and strategies, you can easily conquer the obstacles that come your way. Seeking to further improve your skills? Look no further! Explore our range of bootcamps to study online or at one of our international campuses.

If you’re a person who occasionally goes online (and you’re here, so you must be!) you’ve probably seen news by now about how the ‘tech bubble is bursting’. If you’re also someone who is considering a career in the tech industry (and you’re here, so you could be!) this has maybe given you some cause for concern. We’re not going to sugar coat it. When tech bubbles burst, it’s definitely not good . But is it the end of the industry and of your tech career dreams? Absolutely not! Sensationalist headlines make it look like ‘tech bubble bursting’ is synonymous with ‘entire tech industry collapsing’. But tech bubbles have burst before, and the industry has never stopped. It has only kept on growing. In fact, tech is growing at incredible speeds. Even at the beginning of the latest burst, huge tech companies including Duolingo, Revolut, and countless other startups were still urging new tech applicants to apply. So what’s really going on? First Things First: What is a Tech Bubble? In venture capitalist terms, tech is sexy . Hot new startups with smooth marketing and disruptive products land on the scene and take everyone’s breath away. Success stories of unicorns catch investor’s eyes and everyone wants to be an early adopter of the next big thing. So investors start pouring money into tech companies, hoping to pan for gold. But what ends up happening is that excitement takes over and common sense goes out the window. Companies end up with valuations that are overinflated and the amount of money being pumped into the industry far exceeds the amount of potential revenue it’ll likely give back. The dot com bubble 1990-2002 Time for some internet history! You’ve likely heard about the dot com bubble of the late 90s, a bubble which burst spectacularly in 2002. The 90s really were a different time and the internet wasn’t just new…it was game changing. Which means it was a true feeding frenzy for investors. But not everything was a great idea, despite how much capital investors threw at them. Companies started to crash and investors went running. While not solely responsible, the dot com bubble bursting has been linked to the 2001 stock market crash in the US, which rippled across other territories. While there are aspects of the 2002 bubble that we can see mirrored in the 2022 bubble, it’s true that the tech industry has learned some valuable lessons on how not to run the show. Where did the 2022 tech bubble come from? The 2022 tech bubble (AKA the one that burst last year) actually began in 2011. And it was, reportedly, one of the bubbliest bubbles of all time. A small but mighty group of startups launched their first IPO and reached unicorn status - a startup company that has raised $1 billion USD in funding . This success sent investors into a feeding frenzy and a further $45 billion was thrust at hungry US startups. It was when Facebook went public in 2012 that alarm bells started ringing. At the time, Facebook had only $4 billion in revenue – that’s a lot , but it’s child's play compared to larger corporations. Its IPO became the largest in American history and Facebook was valued at $100 billion. That was the first sign that the tech industry was starting to get out of control. Next came Uber in 2015, which was valued at $51 billion…more than major airlines that had been around for decades. But it was in 2017 that Silicon Valley went truly crazy. Masayoshi Son, Chief Executive at Japan’s Softbank, invested $100 billion alone in Silicon Valley startups. Any warnings about how bubbly the tech scene was getting were soon forgotten and this display of confidence in the tech scene had other investors throwing their hats in the ring. What Happens When a Tech Bubble Bursts? When a tech bubble bursts, it’s certainly not a good thing. But is it the end of the entire industry? No. Far from it. A tech bubble bursts when investors start to realize that the amount of money they’re putting into startups is more than they’ll ever get back. Stocks become less hot and slowly go back to more normal prices. For startups, this means that the amount of cash investment they’ve been working towards is suddenly sliced. Some companies choose to freeze their hiring process in order to cut costs, and some may have to lay off some employees, teams, departments, or even locations. In extreme circumstances they may shut down for good. The tech bubble bursting doesn’t mean that the whole industry is over and done with. It just means that, in investment terms, it’s not the playground that it used to be. Startups have to account for their new valuation and plan their growth accordingly. Smart business owners can smell when the tech bubble is about to burst and try not to get ahead of themselves in their budgeting. What does the 2022 tech bubble mean for the industry? The scariest thing you may have seen from the 2022 tech bubble are the layoffs . If you’re an aspiring techie, it’s understandable that you might feel a bit concerned. We’re not looking to downplay the seriousness of layoffs. Each stat is a real person with bills and a life to pay for, and no one in the industry is happy to see people lose their jobs. That being said, is a tech bubble bursting the end of all potential and opportunity in the industry? Not at all. Many tech recruiters and tech companies are adamant that they still have a need for talent. While exact stats on which roles and experience levels are most impacted by tech layoffs this month, we’re still seeing companies across Europe, Latin America, and the US desperately seeking fresh junior talent. At Ironhack, our hiring partners are still working with us to fill their talent needs. A burst bubble may slow the tech industry down, but it’s far from grinding to a halt. Many companies are still looking for new people to help them grow as they navigate these new waters. How To Get a Tech Job in a Burst Bubble If you’re an aspiring techie, whether you’re choosing a mid-career pivot or a career path to take straight out of school, this means a couple of things. The first thing you’re probably wondering is ‘are my tech career dreams still within reach?’ And the answer to that is yes. But you may have to adapt your job hunting approach to fit the climate. Search for remote tech jobs Jobs are still out there, but it’s true that the competition may get fiercer; this depends on your situation. If you live in the home city of a tech company that has just laid off an engineering team, then they’ve re-entered the local talent market and may be looking for the same jobs as you. If that’s the case, consider broadening your search to remote jobs. Remote positions by nature have a wider talent pool, and aren’t limited to hiring in their local area. This does mean that there will be more applicants, but it also means you get to widen your net as well. Instead of applying for the small handful of jobs available in your hometown, you get to job hunt all over the world. Change your niche If you were aiming to get into Fintech, one of the tech niches most impacted by layoffs so far, you may need to manage your expectations when job hunting. Until recently, Fintech startups were the hottest place to work, with recruiters going crazy trying to fill the gaps on rapidly expanding teams. But with companies like Klarna laying off 10% of its workforce, rivals like Wise and Revolut are swooping in to claim new talent. These companies are still hiring for hundreds of roles, so if you’re really keen to get into this niche and you have the right experience, you can absolutely make your dream a reality. Just be prepared to face fierce competition. There’s few things more valuable in a candidate than experience with a direct competitor. Take on some freelance projects Use this time to expand your portfolio. Not only can freelance work be a good financial bridge between jobs, but it also helps you become better at what you do, and you’ll have more to demonstrate to potential employers. You can get started on freelance marketplaces like Upwork and Fiverr for short term projects, or you can look for 3 and 6 month contracts on more traditional job sites. If you’re looking for remote freelance work (hello digital nomads!) try No Desk . For some tips, check out how Ironhack alumna, Clémence de Robert, launched her career as a freelance UX/UI designer. How to Future Proof Your Skillset Everyone in tech knows how fast things can change, but a tech bubble bursting is something that brings it home. If you’re just breaking into the industry, whether you’ve just graduated from your bootcamp or you’re considering which path to take, the best thing you can do for yourself is to future proof your skill set. That means making sure you’ve got a broad range of skills, some specialist knowledge in your chosen discipline, and hands-on experience to demonstrate your strengths. If you remember nothing else, keep in mind that a tech bubble bursting is absolutely not the end of your tech career dreams. If the world fell apart every time an investment bubble burst, we’d all be living back in the stone age! So stay focused, stay resilient, and make your dream job a reality.

The internet can be a scary place where dangers lurk around every corner, but luckily we have a network of professional superheroes working to defend us and our sensitive data. Cybersecurity is so much more important than many of us realize; without those professionals, the internet would be quite unsafe to use. Before we dive deeper into the challenges that cybersecurity professionals may confront and the tips to deal with them efficiently, let’s take a look at the field of cybersecurity: what is it? Cybersecurity: A Quick Summary Most people have a few thoughts when they hear the word cybersecurity. Security refers to protection and cyber pertains to the internet and technology. Cybersecurity is much more complex than simple protection over the internet. What else does it entail? Let’s dive in: Threat protection The first line of protection against a threat is cybersecurity professionals that prevent them and defend users’ sensitive data. Cybersecurity professionals have a variety of techniques to protect data, including: Encryption Access control and user authentication Intrusion Prevention Systems (IPS) Firewalls Data Loss prevention (DLP) Virtual Private Networks (VPN) Network segmentation Threat detection When hackers do reach sensitive data, it’s important for cybersecurity professionals to know that it has happened so that they can start troubleshooting. For cybersecurity professionals to be notified, they need to use different techniques that will detect a perceived or actual threat and then notify them of it. They may employ techniques such as: Intrusion Detection Systems (IDS) Security Information and Event Management (SIEM) Behavioral analytics Signature-based detection. Once the threat is detected, the final steps are to destroy the malware or corrupted software and respond to the threat with an incident response plan. Threat eradication They’ve detected and analyzed the threat and now it’s time for some action. Hackers sometimes do reach the data and cybersecurity professionals must have a strong incident response plan, which consists of: Preparation: to initiate the plan, the team has to be prepared and ready to set it into motion; this entails frequent simulations, training sessions, and a detailed guide. Identification: at this stage, cybersecurity professionals verify that the data breach occurred and identify how the hacker carried out the cyber attack. Containment: the first thing a cybersecurity professional may want to do when they find the malware is delete it directly, but it’s more beneficial to contain and isolate it to where it can no longer do any damage. This way, they can also study the trail and the malware itself to prevent future data breaches. Eradication: now that the malware has been contained, any and all traces of it need to be deleted too. At this point, security needs to be reinforced and any necessary updates must be applied. Recovery: bringing systems back to their original safe status is the goal of the incident response plan. The business can once again use the system without fear of a further data breach. Post-incident evaluation: the last step is evaluating and analyzing the incident response plan to see where the team could have done better and highlight the wins that the team had. Holding a meeting after the completion of the incident response plan will give the team an opportunity to reflect on the full process together. These three steps function as a continuous cycle for cybersecurity professionals. Most often they are dealing with both threat protection and detection as they focus on maintaining safe networks and defending their clients’ sensitive data; however, threat eradication happens sporadically when there is a sudden data breach or cyber attack. Real world applications of cybersecurity Since our society depends so much on technology and the internet, there’s a huge need for cybersecurity defense and solutions. Where can we see it in action? Check out some examples: Government: countries around the world have hundreds of millions of citizens and each one comes along with a large footprint of data and information. Not only do citizens have their own files and data, but governments have a range of services, organizations, and other entities that they need to keep private. Cybersecurity professionals are constantly protecting this information from falling into the wrong hands because it is so sensitive. Financial services: the financial sector depends highly on the internet to not only store and hold capital, but also to make transactions and interact with the stock market. Cybersecurity professionals must protect financial data because stealing financial data is oftentimes direct theft, or can lead to it. Healthcare: in addition to a large amount of patient data, healthcare uses many IoT, or Internet of Things, devices that, if hacked, would be very dangerous directly to the health of a patient or to their data. The information harvested from hacks past medical portals detail so much about the individual and could be used in a variety of ways to harm them. Cybersecurity is ubiquitously found throughout the internet and given its vastness, there are challenges that go along with it. Nevertheless, where there’s a problem, there’s also a solution. Cybersecurity Challenges The following are the most important cybersecurity challenges that a cybersecurity professional may experience, in addition to our advice about overcoming said challenges. Lack of company resources and management The tech world has expanded exponentially and one of the most available jobs related to tech is cybersecurity. Currently, there are 1 million cybersecurity open positions around the world and because of that, many companies are hiring their first-ever cybersecurity professionals. Walking into a company that doesn’t have an established cybersecurity department can be hard and stressful since it would fall on the new recruit to work with the company to create and design it from the ground up. Ironhack’s tip: being the first one to work as a cybersecurity professional in an office can be scary but incredibly empowering! You have the opportunity to build a department from scratch and the best way to do it is to depend on other contacts. Networking and knowing other people is really helpful in this situation because it’s likely that cybersecurity professionals with more experience have lived a similar experience and can provide you with insight. So reach out! No room for errors As a cybersecurity professional, you may feel pressure to protect data at all costs. It’s a high-stakes situation and this can cause a lot of stress, long, sleepless nights, and other issues in your personal life. Ironhack’s tip: As mentioned above, threat eradication is a part of the cycle and as a cybersecurity professional it’s essential to remember that cyberattacks and data breaches do in fact happen and it’s normal. It’s not a matter of if, but rather when and having that in mind may lighten the mental load. By no means should you relax, but remember that it’s an important job. When it’s impacting your mental or physical health, it’s time to look to reassess. Keeping up with trends Technology is constantly shifting and developing and hackers continue learning and adapting their own strategies to improve their likelihood of reaching and acquiring their target. For cybersecurity professionals, part of their job is staying not only up to date, but also ahead of the curve so that they can keep their company data safe. Ironhack’s tip: consistent education is the key to keeping up. Courses, workshops, conferences, events, magazines, newsletters, podcasts, blogs, and online resources are all mediums for learning about new techniques and dangers. Dedicating two to three hours a week just to reading about these trends can be extremely useful. When there are no data breaches, everything is fine Everything is quiet and there haven't been any cyberattacks. What do you do? It can be easy to relax and think that no noise means that everything is perfectly fine. Unfortunately, in the world of cybersecurity, sometimes no problems may mean the opposite. Ironhack’s tip: it may seem monotonous, but daily threat checks are necessary when working as a cybersecurity professional. Threat detection techniques are essential in these lulls since a data breach may have happened and threat detection techniques will alert cybersecurity professionals to the presence of malware. Constant vigilance of all networks can be facetious, yet they are crucial. They aren’t prepared for a cyberattack Cyberattacks will happen and when hackers do reach that information, the incident response plan should be like second nature; however, that isn’t always the case. Companies may have the plan in place and not execute simulations or practice that may cause a lot of confusion, hesitation, and/or reluctance when it does happen. Ironhack’s tip: although you don’t have control over whether your department employs simulations and practices, you can take it upon yourself to practice on your own. It may not be the same as doing it with your team, but you will at least cover your own tracks and avoid becoming flustered or confused. Similar to all fields in tech, cybersecurity has its challenges and for every hardship or difficulty, there is a way to attack the problem.  If you want to help others, protect their data, and prevent cyberattacks, Ironhack’s Cybersecurity Bootcamp is for you!

Making the decision to learn to code is a big one; you’re investing in your future, learning a skill that’s in high demand and will continue to be for years to come. But when it comes to actually learning to code, there’s a few more decisions to be made: how will you learn? Where do you start? Is one way better than another? We’ll cover the basics of learning to code, as well as three main methods (self-teaching, bootcamps, and computer science degrees), in this article. Let’s dive right in. Why Learn to Code? Every good decision is made with logic behind it; because of this, we need to answer one question before we start explaining the different methods to learning coding: why learn to code in the first place?! That’s easy: The future of coding is bright : web development skills are highly demanded across almost every industry; with coding skills, you’ll have a bright future ahead of you. There are tons of tech jobs available : hundreds of tech jobs go unfilled every year and that’s because there’s a gap between what companies need and what candidates can offer. As a skilled web developer, your application will be valued and you can find the right role for you. Tech salaries are, on average, higher than non-tech salaries : your investment in learning to code will pay off–tech professionals typically earn significantly more than roles in other industries and there’s lots of room for growth, meaning your earning potential is basically limitless. Web development roles offer flexibility : as most tech roles just require a computer, developers benefit from flexibility, such as hybrid and remote options or the option to make your own schedule and hours. You can finetune your creativity and reasoning skills: coding is made up of solving a lot of little, individual problems, such as finding bugs in code. Finding answers to your problems requires logic and reasoning, but also creativity; successful web developers are able to think outside the box and find creative solutions. Now that you’ve decided that coding is for you, it’s time to break down the three major ways to learn to code. Learning to Code: Self-Taught So you’ve decided to teach yourself to code. Wow! That’s a brave choice and one that will require lots of work. Some may tell you it’s impossible and while it will certainly be challenging, it’s definitely an attainable goal. Luckily for you, there’s a large collection of online resources available to you, in addition to books, online courses, and videos. Teaching yourself code is the best choice for you if you can’t attend a bootcamp or get a university degree in computer science, for whatever reason. Maybe you can’t justify the cost or can’t take enough time off from your current role. No matter the reason, make sure you’re fully committed to self-teaching before you get started. Tips for teaching yourself how to code Your coding journey will be much smoother if you: Have a clear understanding of why you’ve decided to learn to code : are you learning to code as a hobby or are you trying to get into tech? If your end goal is a career in tech, then you need to be very thorough in your studies, ensuring you’re ready to enter the workforce. Choose a specific area : just like with anything, coding is broad and there are tons of different areas to learn. Do you want to learn a specific programming language? Or just coding basics? Figure out exactly what you want to learn and then ensure your materials are geared towards that specific area. Plan your learning : if you’re learning with a specific goal in mind, it’s especially important to make a plan. Give yourself checkpoints to make sure you’re learning properly and stay on track. If you find yourself falling behind or missing your goals, take the time to reestablish them and ensure they’re realistic. Remember, you’re learning on your own terms: be flexible with yourself. Practice what you learn : coding is an area where practice truly does make perfect. As you learn new techniques and tools, put them into practice! Try to write your own code, debug it, and learn from your mistakes. Giving yourself tasks throughout your learning journey will also help ensure you’re truly learning. Connect with others : whether your goal is to become a software developer or just learn a little bit about coding, there’s a large network of developers out there and connecting with them will both help you land a job and answer any questions that may arise. Learning to Code: Computer Science Degree Long considered pretty much the only way to learn to code at a level where you’d be able to land a job, lots of today’s web developers have a computer science degree from university, where they spent four or five years studying the concepts and practical aspects of computer science. This path is ideal for those who are heading into university and are sure they want to work as a developer; those who, however, have a degree in another area and are considering returning to school to get a second degree or get their first at a later age should seriously consider the financial and time commitments of a four or five year degree program. Tips for studying a computer science degree As you begin your computer science journey, keep these tips in mind: Practice constantly : especially at the beginning, your classes will likely consist of lectures and theoretical work. To ensure you’re keeping up with what’s being taught, give yourself lots of practice on the side. This will help you not only advance in your coding abilities, but also guarantee you’re not falling behind. Connect with your classmates : networking in tech is very important; make connections with your classmates to get assistance when you need it and have contacts post-graduation. Avoid last-minute cramming sessions : programming is complex and the skills you need to succeed can’t be learned in just a few hours before a test. Make sure you’re keeping up with assignments and test prep throughout the entire semester and use your last study session as a review, not the time to learn everything. Stay up-to-date with the tech industry: tech is evolving incredibly fast and you need to know what’s coming next! Check out what’s happening in tech, the newest tools and technologies that come into the market, and, of course, what employers are looking for in candidates. Familiarize yourself with general computer knowledge : sure, you don’t need to fully understand computer hardware to write code, but it certainly helps. Practical computer skills, such as how computers work and how they’ve evolved over time, can provide you with additional and valuable knowledge when it comes to working as a developer. Learning to Code: Bootcamps Our last option is our favorite (shocking, right?!), but that’s because bootcamps are incredible ways to learn to code quickly and efficiently without the financial or time commitment that university degrees demand and a guided and structured curriculum that’s designed by experts. Typically taking place over the course of just a few weeks or months, bootcamps are highly intensive and focus on preparing you to enter the workforce immediately after graduation. Bootcamps are ideal for those looking to make a career change and can’t commit to a four year degree, but want the structure and guidance that self-teaching can’t offer. Because they’re focused on this specific group of career changers, bootcamps usually boast flexible options, such as remote, in-person, full-time, or part-time options, in addition to additional services like career support. Tips for taking a bootcamp To succeed in your bootcamp, keep these tips in mind: Properly prepare for the bootcamp : as we mentioned above, bootcamps are quite the time commitment, even if you choose a remote or part-time option. Prepare yourself for intense focus during class and studying in your free time; if you have a super busy life outside of the course, it may be nearly impossible to keep up. Making the decision to take a bootcamp also means being honest with yourself and your current responsibilities. Take advantage of all the bootcamp offers : does your bootcamp have income share agreements? Or payment plans? Or maybe interview and CV prep? Many bootcamps, thanks to their main goal of landing you a job immediately after graduation, offer additional services–use them! Network, network, network : bootcamp instructors are usually skilled and experienced industry leaders and are fantastic connections to have as you enter the tech world. Take the time to also connect with your fellow classmates; they’re also about to enter the job market and could be valuable contacts in the future. Complete the pre-work : no matter your level of experience in coding, complete any pre-work that your bootcamp assigns. Even though it can be annoying to have tasks before you even start the course, starting the bootcamp with the necessary foundational knowledge is absolutely key. Take your time and complete the assignments–you’ll thank yourself later. Prepare your CV and interview skills : switching careers is a big step; the more preparation you do during the bootcamp, the better off you’ll be once you start applying for jobs. Start building your portfolio from day one, updating your CV as you master new skills. As you can see, there are lots of ways to learn to code and the choice lies with you and your specific situation. But no matter what you choose, remember this: coding is an incredibly fast growing field with tons of opportunities and if you take the time to properly learn what you need to know, you’ll have the chance to truly transform your career. Ready to get started on that life changing decision? Our Web Development Bootcamp has everything you need:

UX/UI design has become evermore important as the tech industry grows and companies are seeing the need to center user experience and the importance of creating user-friendly interfaces. Designers will undoubtedly face challenges while creating websites and web applications; that said, knowing how to not crack under the stress of those challenges is the task for UX/UI designers. We’re going to look over a couple common bumps in the road for UX/UI designers and offer our two cents in navigating those roadblocks. UX/UI Design: a Quick Summary User Experience (UX) design and User Interface (UI) design are two closely related fields that center users in creating web applications and digital interfaces. Although the terms are often used interchangeably, UX design and UI design are two distinct areas of website and web application design. UX design UX design is focused on the user experience while utilizing a given website or application. UX designers concentrate on user satisfaction and look to optimize a web app’s functionality. Similarly, UX designers create wireframes and prototypes that offer a structure and form,  ensuring that the application is more user-friendly and intuitive. UI design UI design, on the other hand, is focused on the visual aspects of website and interface design. Their work zeros in on what is visually pleasing to users, meaning they focus on everything from color schemes to page layouts, fonts, and other interactive elements like buttons that will visually enhance a user’s experience. What Responsibilities do UX/UI Designers Have? UX and UI designers play a crucial role in the final result of web design and user experience while utilizing those products. Their responsibilities range from research to coordination to design and product testing, with the following highlighted: User research: UX/UI designers must carry out their own user research in order to get a stronger idea about user needs, behaviors, and preferences. This research may involve interviews, surveys, and usability testing to inform design decisions. Information architecture : designers must organize website information into a logical and easy-to-understand structure such that users are not overwhelmed. They create sitemaps, user flows, and navigation systems that ensure easy access to desired content and features. Wireframing and prototyping: UX/UI designers develop wireframes, low-fidelity visual representations of an interface, in order to secure user feedback on the design. Equally, they’ll create prototypes in order to stimulate the user experience, gain insights from those simulations, and adapt the designs accordingly. Scenarios and testing: UX/UI designers must adapt their work in relation to what their perceived users are looking for. This research is achieved through creating fictional user personas in order to better understand target groups, and simulating user interaction with the interface to identify the effectiveness of the design, user path points, and secure other feedback which then informs design improvements. Communication and coordination: a crucial component of UX/UI design is communication and coordination with stakeholders, developers, and other team members. Whether they be communicating design concepts, debating solutions to a design challenge, or simply working to get a shared understanding of the design process, it’s one of their most important responsibilities. Ultimately UX/UI designer’s responsibilities will vary based on the organization, project scope, team structure, and current company needs, but these are some of the basics that structure a UX/UI designer’s work. Challenges in UX/UI Design UX/UI design work is no walk in the park. There are very high expectations on UX/Ui designers, there’s a lot of competition in the field, trends are always changing, and the work has its fair share of challenges. Here are a few of the biggest challenges UX/UI designers will face and Ironhack’s tips in addressing them. Straddling originality with usability The pressures on UX/UI designers to create something new and unique is great, but often the decision to focus deeply on a visually appealing design or to create unheard of features compromises the quality of the user-friendly aspect of the web application. Even if someone is drawn to the visual aspect of the design, if the product proves to be difficult to use, it’s useless. Ironhack’s tip: designers should not shy away from replicating designs that have proven to satisfy user needs and demands. This means doing the necessary research to understand your target audience, who your users are or will be, creating scenarios, and working to design a product that fits their needs. Once you’re sure that the features and navigation systems will appeal to your target group, you can add your special touch or experiment with a couple new features. Of course you should embrace creativity, but do so hand in hand with research and testing such that both use and aesthetic goals are met. Staying up-to-date on the latest trends Technology and design techniques are constanding changing, and there are always more and more tech tools that can give you the competitive advantage as you improve your products. Staying up-to-date on these recent tools, programs, and designs trends is crucial to a UX/UI designer’s success. Ironhack’s tip: UX/UI designers should find ways to keep up with the latest tech trends. Ironhack has a number of recommendations on how to stay up-to-date with tech industry developments on our blog. We’d recommend regularly reading blogs, finding podcasts, or enrolling in courses to stay informed and overcome the challenge that the quickly developing industry imposes on tech professionals. UX/UI coding challenges Undoubtedly, UX/UI designers will face new coding challenges. This may be because of the complexities surrounding designing for multiple platforms and devices, or simply because designers often face technical constraints while working with developers to implement their designs. Ironhack tip: of course UX/UI designers will always face coding challenges. In order to be successful, UX/UI designers simply need to practice and train their skills in order to remain sharp and not narrow their vision of what’s possible by simply failing to dedicate time to upskilling and reskilling their skills. This means carving out time to practice with real-world coding exercises, training yourself in crucial skills and new tools, and creating portfolio-worthy deliverables. At the same time, it requires strong collaboration and communication with developers, customers, and other stakeholders. UX/UI design ethical challenges Today, there are more and more ethical questions when it comes to UX/UI design given questions surrounding data protection, transparency, privacy, creating inclusive designs, and environmental sustainability. Designers have more ethical challenges than ever and need to be smart in how they go about addressing those potential challenges. Ironhack tip: UX/UI designers must take ethical challenges seriously and work to prioritize the well-being of users, create culturally sensitive and inclusive designs, and design considering the impact of energy consumption, and electronic waste. Equally, data protection must be taken seriously, you can check out our article on how UX/UI designers can collaborate with cybersecurity professionals to ensure the integrity and security of their designs. These challenges are a testament to the complexities and multidisciplinary aspects of UX/UI design. Addressing these challenges head on is crucial to a UX/UI designer’s success. Doing so will require research, creativity, collaboration and a user-centric approach. If you’re interested in learning more about UX/UI design, check out our blog!

Web development is one of, if not the , most famous areas of tech. And those who work in web development are faced with a large responsibility: creating high-quality and user-friendly websites and apps that benefit both the client and the company. As you know, with great power comes great responsibility and web developers have so many options when it comes to web design. From design choices to debugging code, web developers face quite a wide array of challenges in their day-to-day life. And for those who aren’t prepared to tackle said challenges, the field can seem daunting. But what challenges can web developers expect and how can they overcome them? And what do web developers need to keep in mind when preparing for hurdles? We’ll cover these questions and much more in this article. Web Development: a Quick Summary Before we dive into the challenges that web developers face, let’s make sure you have a clear and up-to-date understanding of web development and what it is. As we mentioned before, web development is an incredibly broad field, but one that can be classified into three main categories: front-end, back-end, and fullstack web development. Front-end web development : front-end developers work on the client facing side and focus primarily on ensuring that the customer has the best possible experience while on the website or app, creating user-friendly and intuitive designs. Back-end web development : if you think of coding when you think of web development, you’re probably thinking of back-end development, which is where all the magic happens. Back-end developers build and test the code that makes up the website or app’s framework, ensuring it can run properly. Fullstack web development: for those who can’t decide between the front or back ends, fullstack web developers work on both the front and back ends to create the ideal user experience. There are lots of other areas in web development however: client-side scripting, product management, network security management, and content management system development are all part of web development. When properly used, web development can lead to the following: Increased web traffic: well-designed websites are inviting to customers and make them more likely to both visit the website and stay on it. And this goes without saying, but clients who spend more time on the site will probably end up purchasing your product or service. Enhanced security: websites collect data from clients every time they visit, regardless of if they make a purchase or not. Websites that are properly built and protected safeguard client and company personal information. Expanded reach : well-functioning websites with options for different languages or settings can help your company take off internationally or simply reach more people in more places. Applications of web development Web development can seem a bit abstract, but it’s actually all around us, constantly present in our daily lives: Social networks : have you ever wondered how Facebook knows to update you with a news article you’d be interested in, or Instagram suggests accounts you should follow? Web developers design websites so that client preferences are taken into account and updated on their own, providing the user with the most personalized experience possible. Streaming sites : Netflix always knows the next show to recommend, right? But that’s not all: streaming sites can work on a wide range of devices, allowing you to pick up where you left off during your last show or even downloading content to watch when you’re offline. Document sharing : Google Docs’ popularity has skyrocketed, thanks to its ability to update in real time with other users, letting you edit at the same time as other users. But because it’s so easy to share content across Google Drive, Google’s developers had to prioritize security and confidentiality, while providing their users with the best possible experience. Challenges in Web Development You have a clear idea of web development and what web developers do to create an ideal user experience. But what sort of challenges do web developers face? Do they differ based on area? What can they do to combat these difficulties? Let’s dive into some of the most common challenges in web development and how to overcome them. Managing a large team Web development teams are usually quite large, consisting of front-end developers, back-end developers, back-end developers, tech leads, UX/UI designers, and much more. Each of these people plays a crucial role on the team, working on individual parts of an entire project, meaning that communication and teamwork is absolutely essential. Ironhack’s tip: are you worried about everyone working towards a common goal? It can be hard to keep track of a large team--after all, the last thing you want is to bring the project together at the end of a sprint and find out that one team member misunderstood their assignment. Scheduling weekly or even biweekly check-ins to ensure everyone is on the same page may seem tedious and unnecessary now, but will save you a lot of time and stress later on. Balancing client requests with reality When you start a web development project, you’ll be given instructions from the client: what are they looking for? What problems are they looking to solve? What are their overall goals? Having this information clearly defined is definitely a plus, don’t get us wrong. Frequently, however, clients may have unrealistic or too high expectations that a web development team simply can’t meet. Ironhack’s tip: hear your client out and consider what is important to them. Once you understand their goals, take a moment to think about their wishes from your side, evaluate the realisticness of them, and come up with your own proposal. It’s important to be honest with the client from day one and tell them what’s viable and what’s not; make sure you communicate to the client that you understand their goals and bring your own, adapted version of them to the table. Creating safe web applications There’s some really cool stuff you can do with web application design; skilled developers can create almost anything. But there’s one thing that can’t be compromised: security. Cyber threats are progressing daily and they’re more and more realistic. How can you ensure your client data and web design is protected? Ironhack’s tip: security needs to be your absolute priority when it comes to web design. Instead of finishing your design and then looking at securing your site, keep cybersecurity in mind from the beginning. And if you’re unsure about how to properly secure your site, reach out to cybersecurity professionals to get their expert opinion. Just remember: nothing is more important than security. Finding that sweet spot between the front and back end As we discussed before, the front end works on the side the client will see: the designs, font choices, style, and overall optics of the site or application, while the back end handles the communication between the code that allows the app to function. The perfect web design balances these two perfectly, creating simultaneously functional and inviting designs. However, these are two separate roles and the developers can have opposite visions. Ironhack’s tip: it’s better to check in too much than not enough! Make sure your front and back end developers are working together, bouncing ideas off each other and reaching a consensus about the best decision. There will be times when one has to sacrifice their idea to agree with their colleague; that sweet spot comes with lots of communication and overall compromise. Keeping up with the latest trends We know that tech is advancing rapidly and web development is no exception. In fact, so many new trends and tools are entering the market that keeping up feels impossible. But knowing the newest updates and happenings are absolutely essential in web development. Ironhack’s tip: struggling to keep up or find the time to learn a new programming language? What about understanding the newest cybersecurity threat? In such a fast moving industry, that’s totally normal! And no one expects you to know everything; however, make sure you carve out a few hours a week to read about the sector and any new developments. After all, you never know when that information will come in handy. We’re not going to lie: being a web developer comes with its own set of specific challenges and all aspiring developers should be ready to take them on. But they’re totally manageable! That’s why web development is one of the fastest growing industries . If you’re interested in joining the fight and taking on some of these challenges head on, Ironhack is the perfect place to get your start in web development. Ready?! We can’t wait to see you in class.

You know how important data analytics is to your company’s success. From predicting the success of future campaigns to seeing what’s not working, every company uses data to make better decisions. And while that might seem straightforward, we have bad news: it’s not! Using data to make smart decisions can be challenging , especially for those who haven’t had to depend on data in the past. If this resonates with you, you’re in the right place! In this article, we’ll cover some of the crucial things to keep in mind when running a data analysis, common challenges you’ll come across, and, of course, some helpful tips and tricks to help you overcome them. Ready?! Let’s go. Data Analytics: a Quick Summary Before we dive into the challenges you’ll face when working in data analytics, we should quickly define data analytics, why it’s useful, and its common applications. Data analytics is the process of turning raw information into actionable insights, drawing conclusions and insights from various sources to make better decisions. The benefits of data analytics are numerous, but let’s cover a few: Better decision making : we mentioned this one from the very beginning; looking at past results and future predictions can guide your decision-making process, making it more effective. Data analytics tools, which are capable of handling much more data than humans can, can also help identify trends and patterns that maybe weren’t visible to the human eye. Personalized customer experiences : clients want to feel special and want to feel like their voice is heard, even though they’re just one shopper in a group of thousands. Companies that use data to analyze customer behavior can both suggest similar products or services to their customers and find out what they are interested in, ensuring they remain clients and return to the website/store. Higher efficiency : data isn’t just used for decision making about the future; it can also  be used to find mistakes that were made in the past and identify why they occurred. In addition, it can use past data to highlight problem areas and suggest improvements. Enhanced security : part of using data is ensuring that this valuable information is properly stored and protected. Data analysis tools can help you identify vulnerabilities in your network and predict future problems so that you’re able to get ahead of them. As you now know, data analysis is a crucial part of any company’s business processes. But how is data analytics actually used in the day-to-day of companies? Below we’ve highlighted four sectors where data analytics has transformed the industry’s overall functionality, providing valuable insights to leaders. Applications of data analytics Data is used in every sector, but here are four where its impact has been (and will continue to be!) monumental: Transportation : have you ever wondered how the maps app on your phone continuously updates, telling you how to avoid roadblocks or traffic jams? Well, it’s because of data! Your phone receives real-time information about the state of the roads ( data! ) and analyzes it to suggest the best possible route for you. Advertising : no, your phone isn’t listening to you. It may seem impossible that Amazon always knows what you want to search for, but it’s purely data! Through collecting data about what you’re searching for and others have searched for when buying similar items, websites are able to provide you with incredibly personalized experiences to enhance the customer experience. Security : businesses receive lots of data these days and things can fall through the cracks. Security, however, is an aspect of business that cannot be ignored; data analytics tools can help improve security, finding areas to keep an eye on or flagging troubling patterns in the data. Marketing : one of the first steps that companies need to take when creating their product or service is deciding who their ideal customer is; through collected data, companies can learn more about their clientele, such as their gender, age, race, or socioeconomic status, which can help them provide a more personalized customer experience. Now that we’ve properly outlined data analytics and why it’s so important, let’s dive right into challenges you could face–and solutions, of course. Challenges in Data Analytics Especially as they get used to working with data, companies can face significant challenges when it comes to using data analytics properly. After all, with such large amounts of data, there’s a lot to handle. Let’s discuss five of the biggest challenges facing data analysts and give you some tips to overcome them. Thinking data has all the answers We know you’ve heard us talk about data and how valuable it is; you may think it’s almost magical. And while it can certainly provide incredible insights to your company, it’s not all-knowing and only properly trained data professionals truly know how to make the most of it. A large part of successful data analytics is knowing how to sort and categorize data so that you’re able to draw the conclusions you actually need. Ironhack’s tip: before you even begin your analysis, have a clear understanding of what you’re trying to accomplish. Then break the data down into smaller areas that will help you get the insights you’re after. Trying to look at the data all at once is impossible and won’t bring you to any truly helpful conclusions. Trusting data analytics with just anyone How hard can reading a few graphs be anyways?! Well, quite difficult, actually. Data analytics should be entrusted exclusively to data professionals who have experience in all sorts of analysis and can help work through the large amounts of data to find what really matters. Ironhack’s tip: if you don’t have anyone at your company who can run data analysis properly, it’s best to outsource to a true data professional, or look to up or reskill current employees through bootcamps or online courses. It’s an investment, but one that will positively benefit your company in the future. Struggling with data visualization Your data professionals have gone through the data and claim to have insights, but when you take a look, it doesn’t make any sense. Or you’re trying to make a point to your higher-ups or another team and they’re just not understanding what you’re trying to communicate. Here is where data visualization comes into play; your conclusions are useless if others can’t understand them. Ironhack’s tip: commit to finding the right data visualization tools for you and your company, educating your team on how to use them, and ensuring that the data is properly displayed. It may take up some time now, but your future insights will be better understood and implemented. Collecting low quality data Insights from data are valuable–as long as that data is accurate. When it comes to low quality data, which could be incomplete or inaccurate, it can seriously affect your final decisions and lead to problems. Little mistakes or problems seem minor when looking at just one part of the data, but when combined with other errors, can lead to completely skewed data. Ironhack’s tip: lots of mistakes and errors come from manual human work. Automating tasks such as data entry or using tools to find any missing information can both free up your time and ensure that human mistakes are eliminated. There are tons of data softwares and solutions out there that are designed to facilitate your data entry process, flagging any possible errors. Trying to communicate the importance of data There’s nothing worse than trying to talk to someone who just won’t listen. And unfortunately, there are lots of people out there who negate the power of data, and especially when it goes against long-established practices. One of the biggest challenges that data professionals face is trying to instill the same respect for and dependence on data that they have to other departments or even company leaders. Ironhack’s tip: find out what’s important to your colleagues and show them how using data will benefit their specific needs. Once you’ve convinced them that it will help with what matters most to them, you’ll be able to slowly show them how data works in other areas. We could go on and on–after all, data is such an important part of everything we do. But we don’t want to bore you! If you’re interested in harnessing the power of data and improving business processes, your future may lie in data analytics. Luckily for you, Ironhack’s Data Analytics Bootcamp teaches everything you need to succeed. Interested? Check it out below.

These days it seems like you can do anything online, right? From online shopping to doctors appointments, we can take workout classes from our homes and have our groceries delivered to our door. And during the COVID-19 pandemic, another online option quickly became quite crucial: online learning. Imagine being able to attend university or, better yet, take a quick online course from your sofa or bed? While online courses did exist before the pandemic, they weren’t a widely accepted option and lots of people doubted their effectiveness. After all, how can you guarantee that online courses provide the same education as in-person classes? Or replace the human interaction of working with classmates? The world was forced to quickly come to terms with online learning in March of 2020 and since then, we’ve been able to revolutionize remote learning , making taking online courses accessible for even more people. Today, you can take courses in practically anything–the sky (or the cloud?!) is your limit. Remote courses also boast the following benefits: Increased flexibility : are you interested in learning a new skill but can’t afford to give up your current job? Or have other duties, such as caring for an elderly family member or children? What about if you’re only free at night? Remote courses make learning more accessible, thanks to their flexibility. If you choose to take an asynchronous course (read about that below!), you’ll have even more flexibility. Reduced overall costs: the cost of attending a bootcamp online might be just slightly cheaper than an in-person option, or maybe the same price, but you’ll save valuable time and money from not having to commute. And if you are choosing to take the course remotely because of other commitments, such as childcare or work responsibilities, you may save money there as well by not having to give those up. Expanded options : maybe your dream course isn’t available anywhere near you, meaning you’re unable to take it. Online courses open up your options, permitting you to take courses from anywhere in the world (keeping time zones and language in mind, of course!). And if your chosen bootcamp is nearby but has a schedule that doesn’t match your availability, you can make it work remotely. Accelerated preparation for the real world : lots of today’s jobs are either hybrid or entirely remote: do you have what it takes to be totally autonomous and work well from your home? By taking an online course, you can prepare yourself for working remotely, ensuring you’re prepared to enter the remote workforce. Enhanced accessibility : people can be deterred from taking in-person courses for a variety of reasons: social anxiety, learning disabilities, physical limitations, and much more. Remote courses make these bootcamps accessible for everyone. Improved access : did you space out during a class and need to revisit a lecture? Or are you looking to look at the assignment you submitted last week? Because online courses use tools to conduct the class online, you have all materials and lectures in one place. Now that you know why remote bootcamps are gaining traction across the world, let’s define them a bit more. Online bootcamps come in two forms: Synchronous : synchronous bootcamps, or live bootcamps, are conducted as if you were in the classroom, with a firm schedule and requirements to be online at certain times. For those who can’t attend an in-person bootcamp but still desire structure, this is the best choice. Keep in mind, however, that online bootcamps are offered all around the world and find one within (or close to) your own time zone. Asynchronous : for those looking for even more flexibility, asynchronous bootcamps don’t require you to be online at specific times, allowing you to watch recorded lectures and classes on your own time. While this makes the bootcamp more accessible for those with tough or inconsistent schedules, it demands a certain type of accountability and motivation to ensure you get the work completed on your own. One of the most popular online courses is coding , due to the high demand for tech professionals in practically every market. With a clear understanding of why online bootcamps are a great option, let’s head into remote coding bootcamps. Learning to Code Online Coding bootcamps offer students the chance to learn the basics of web development in just a few weeks from the comfort of their own home. And as coding skills become widely demanded across a wide range of industries, not just tech, there’s never been a better way to invest in your future. Learning to code can: Increase your future job opportunities : as we mentioned above, coding is needed in so many industries and the number of available positions is only expected to increase. No matter what your preferred industry is, you’ll be able to find a sector that fits with your skills and your passions. Increase your earning potential: you’ve heard that tech jobs pay well and there’s a reason for that: they do. Salaries for tech professionals soar above those of non-tech roles; tech roles usually have room for growth as well, meaning you can increase your earnings in the future as well. Offer flexibility : lots of tech jobs are remote and taking on a coding job can mean you’re able to make your own hours or simply work from the comfort of your own home. This can benefit your overall well-being, but also save you commuting time or allow you to spend more time with your loved ones. But coding’s flexibility isn’t limited to just your schedule; it also allows for career movement, permitting you to move from sector to sector or even try new programming languages or skills. Improve your critical thinking skills : learning to code isn’t just choosing a programming language and setting up a website’s design. It consists of lots of little problems that each require a different solution and thought process. Learning how to handle these kinds of problems will help you in all areas of your life, not just coding. Spark your creativity : tech is advancing rapidly and you won’t just learn to code once! Because there’s always a new tool or update to review, you’ll be constantly challenged throughout your career. And in your free time, you’ll be able to explore new things, keeping your creative juices flowing. Is learning to code online actually worth it? We get it–it can be hard to understand how learning online can be the same as learning in a classroom. But thanks to recent advances in technology, learning online is incredibly similar to traditional classroom instruction; you can still interact with classmates, chat with them, and even do a virtual happy hour after class. Just like with anything, you have to determine if online learning is right for you. There’s no instructor in front of you, telling you exactly what you need to do and making sure you stay focused. And tasks must be completed on your own, demanding a certain sense of independence and time management. Here’s the key: as long as you’re confident in your ability to take the course seriously and complete your assignments, online coding courses are a fantastic option. But how do you actually go about it? Luckily for you, there are a few different ways. How to learn to code online Now that you’ve decided to make that jump into learning to code online, it’s time to decide exactly how you want to do it. Let’s break down a few of the options: Online videos You can learn practically anything on YouTube and coding is no exception. Depending on your previous knowledge of coding, this can be a great place to start, especially for more visual learners. Lots of online content creators have uploaded videos that cover the basics of coding or tackle more specific problems that you may face; a simple Google search can lead you to where you need to be. Chatrooms Web developers make up a large community online where they go to trade ideas, troubleshoot, and learn from each other. This is an option for more experienced coders who are either looking for very specific assistance or want to get some new ideas for a problem they’re facing. From GitHub to Reddit, almost every web developer uses online communities or chatrooms in their work. Short courses If you already have a background in tech or web development, or maybe even coding, and are just looking to hone one particular skill or take on a new programming language, short, topic-specific courses are a fantastic option for you. Some are free and some require payment; review exactly what these courses offer and see if they fit into your goals. Bootcamps We’ve arrived at our preferred method for learning to code online: bootcamps . The aforementioned ways to learn online are certainly great options, but not as thorough or helpful as a full bootcamp. Bootcamps are intensive and relatively short (think a few weeks or months) courses that focus on one topic in particular, providing you with personalized and detailed instruction. Well-designed bootcamps usually offer additional services, such as career or financing assistance. Learning to Code Online with Ironhack At Ironhack, we recognize the importance of flexibility when it comes to bootcamp learning and that’s exactly why we offer remote (and in-person!) options for our web development, UX/UI design, data analytics, and cybersecurity courses, in addition to: Full and part-time options to ensure that you can either maintain your current responsibilities/job or dedicate your full attention to the bootcamp. Career Services that last beyond your time at Ironhack; we help you polish your interview skills during your bootcamp, but also continue our support throughout your first year as an alum. Financing options to make our bootcamps accessible to all; check out our income share agreements or payment plans. Sounds good, right?! We’re fully committed to ensuring our remote students receive a high-quality education and graduate ready to enter the job market. If you’re ready to become a coding expert and land your dream job in tech, Ironhack is the right place to start. We can’t wait to see you in our (online!) classrooms!

Have you ever gone to a website and thought that it was confusing or difficult to navigate? Or maybe you go to an application that amazes you because it’s so simple to browse, beautiful, or intuitive. When we interact with a website or application, we don’t even realize all the thought and effort that goes into all of the elements with which we interact. To understand all the intricacies of creating an engaging website or application, let’s start with the basics: the user interface definition. What is a User Interface? How can you define user interface? Accessing a website or tapping on an application, you, the user, are interacting with the interface. The user interface is the interaction between the user and a digital system or product and there are various mediums through which they interact: Mouse Screen Keyboard Desktop When you are using this hardware, you are making contact with the user interface, which as you already know, can be wildly diverse and varied. There are an infinite amount of designs that a user interface can have, but is a user interface design made up of? What is User Interface Design? A lot of elements make up the software that constitute the user interface design. Some of these components include: Design elements: everything that goes into making the website aesthetically-pleasing falls under this core element of the user interface: typography, fonts, color palette, icons, images, and other visual elements. Input controls:w hen you input data or trigger actions, you are interacting with the input controls, like buttons, dropdown menus, toggles, text fields, and checkboxes. Navigational components: to move around the website or application or even to go to another source outside of it, the user has to make contact with a navigational component: search bars, tabs, links, and breadcrumbs. Feedback notifications: sometimes a user interface attempts to notify you of an action you have taken and when they are informing you of this feedback, they rely on elements such as messages, tooltips, progress bars, audio or visual cues, loading spinners, and other notifications. Interactive elements: fostering engagement and interactivity is important for a user interface and that is the goal of components like hover effects, animations, and transitions. Both UX and UI designers coordinate their efforts to create an enjoyable, intuitive experience . Although they are responsible for different elements, they need to come to an agreement on much of not only what the interface will represent and display, but also how it will connect with the user. UX designers’ responsibilities UX design is one half of the user interface formula and it focuses less on the visual design elements and more on the structure and flow of the design. Not only do they create and design the user flow and layout, but also gather feedback from users to see how they can further improve their designs. Their role is essential to ensure that the final product is simple, intuitive, and engaging . UI designers’ responsibilities On the other end, UI or user interface designers are artists that focus on aesthetic of the website or application. They want the user to interact with a pleasant experience on the page without feeling overwhelmed or confused . UX and UI designers must collaborate on their projects so that they can fully realize the final product and ensure that they have created the most well-rounded website or application. Since their shared goal is to make the experience dynamic and engaging for the user, let’s check out some tips for UX/UI designers in their process to design a more engaging user interface. 10 Tips for Create Engaging User Interfaces Understand your users It is extremely important for the UX/UI design team to collect as much feedback and data as possible throughout the entire design process and to create user personas; this way, the whole team will have the most relevant data to make the right decisions . Connect emotionally Humans are emotional beings and when a user goes to a website or application, they are inadvertently emotionally interacting with a page. It may start off as just simply like or dislike; however, the experience can become even more emotionally charged depending on the product too. The UX/UI design team has the responsibility of creating this emotional bond through the medium of their interface through certain images or colors. Keep it simple, clean, and consistent Have you ever visited a website that seems loud and overly complicated? Some of us are perfectionists and that can cause us to want to do the most for the user interface and give it extra functionality. However, it’s important to remember that customers are there to engage with the product, buy it, and continue their day. Because of that, simplicity and consistency are key points . You want to guide your customer from the homepage to the product as simply as possible. Tell a story In addition to emotions being something very human, a powerful story is a significant part of the human experience that has accompanied humanity since our beginning; a strong story is hard to resist , just like any book or movie. Consider some of the following questions: Why does the application boast a color palette of red, white, and black? How does that image relate to the mission statement on the previous page? Why is the typography in the title different from that of the description below? These questions are also linked to the branding and using the brand identity as guidance can help that story take shape. Define clear user flows You have to have a clear progression of events and possibilities that the user will experience on their path; creating straightforward user flows will help them connect with the page. Relating back to designing a simple and clean design, clear user flows focus the user on their goals since there are no distractions that may deter them from their objective of purchasing the product. Planning the flow beforehand is essential to provide that structure to not only the design team, but also the client and users. Employ interactive elements There are five types of elements that appear in UX/UI design as parts of the user interface and the one that most relates to user engagement are the interactive elements. Sprinkling some of these into your user interface design can add some interactivity and spice up the user experience. Remember that these interactive elements, while engaging for the user, can also overstimulate them if abused . Use trending designs Sometimes the best way to enhance your own work is to look to the world for inspiration . A novel and exciting trend can be just what the doctor ordered and finding this inspiration can be as easy as looking at peers’ websites or downloading some of the most popular applications. Some trending designs in 2023 include: Buttonless user interfaces Minimalism Big and bold typography Dark mode Advanced micro-interactions Augmented and virtual reality Simple authentication Maximize device and cross-platform functionality Some interface user designs don’t maximize the functionality when appearing on some devices. Ensure that the webpage or application can be used in both horizontal and vertical orientations on a cell phone or tablet. It can be frustrating for users to turn their cell phone to the side and the layout to go bonkers and in other instances of not maximizing the device functionality, the user may feel that same frustration. Make sure that you are thinking of all the ways that users interact with these devices and across several platforms when creating these designs. Use space smartly Whitespace is a part of all websites and applications that may not be an element that we think of much since it is the opposite of content; however, having a balance of content to blank space is crucial to the user interface design. If there is not enough whitespace on a website or application, it can feel busy and if there is too much, then the design can feel boring. Another important part of having this balance is to highlight and make the essential information stick out boldly. The next design you make, remember to think about not only the content, but also the whitespace. Create intuitive navigation Clunky websites or applications that don’t flow or need to be figured out by the user can be a drag. People want to visit the page and have a carefree, easy experience to buy the product or access the information they want and move onto their next task. Navigation between pages needs to feel natural instead of frustrating or overly complex. For all of these tips, a great way to ensure that you’re on the right track and hitting all of these points is to ask questions on user surveys: Does the navigation feel intuitive? What’s the story that this website tells you? How would you rate the simplicity of the website? These and other questions will help confirm if the user can see how you’ve reflected these elements in the user interface. UX and UI designers create user interface designs every day and want to maximize the experience for the user through the art and stories that they curate. If combining these two along with technical skills sounds like it’ll fill your cup, take a look at our courses page and sign up to become not just a UX/UI designer, but also an artist and storyteller .

We know that businesses are heavily reliant upon data analysis and new technologies in determining their organizational strategies and developing their products and services. As tech professionals, we hear terms like machine learning (ML), artificial intelligence (AI), automation, algorithms, and data on the daily. Understanding these technologies and learning how to harness the power of these tools allows organizations to make stronger data-driven decisions and secure a competitive edge in their industry. Data analysis has been around for centuries, beginning with the development of Business Intelligence (BI) in the 1800s. However, data analysis has greatly transformed with the introduction of new technologies and ways of organizing, breaking down, analyzing, and securing actionable insights from said data. Machine learning has played a crucial role in those developments. Machine learning is used to automate the data analysis process and aids the workflow in order to arrive at deeper and more holistic insights . Let’s break down each area of work and from there we’ll gain a better understanding of how companies can utilize these technologies to make stronger decisions and become more competitive organizations. What is Data Analysis? Data analysis involves examining and interpreting large amounts of data with the intention of identifying meaningful patterns and trends. Through uncovering valuable insights from data , organizations are more capable of making strong, data-informed decisions. Data analysis ultimately can help sharpen business strategies, improve products or services, or expand an organization’s understanding of their clientele. Data analysis today relies heavily on various tools and techniques to aid the data analysis and visualization process. These techniques may include statistical analysis, machine learning algorithms, and data visualization methods . Through these techniques, organizations can gain valuable insights that will help in optimizing their operations. This information can later inform business decisions, help cater marketing campaigns to specific consumer bases, enhance products and services , and lead to other actions that strengthen the organization. What Responsibilities do Data Analysts have? A data analyst’s role is to transform unstructured and unorganized data into valuable insights that inform a company’s data-driven decisions. A data analyst’s actual work may vary across organizations and fields of work, yet, the essence of utilizing a variety of tools and techniques in order to make sense of messy data remains the core task for these tech professionals. In other words, their core tasks include: Data collection and organization : data analysts gather data from multiple sources and structure it in a format suitable for analysis. Data cleaning: once data is collected and organized, analysts must “clean” the data to ensure that it’s accurate, consistent, and free of errors or missing values. Data analysis: through the use of various data analysis tools and techniques, data analysts examine data to uncover patterns, trends, and other valuable insights. They often leverage machine learning algorithms to build predictive models that support in gaining a deeper understanding of the data and help data analysts make stronger recommendations. Exploratory data analysis (EDA): is a standard data analysis approach that looks to uncover fundamental patterns, trends, relationships, and irregularities in the data. It relies upon an initial data exploration phase, weaves in statistical analysis, and often utilizes Python functions to manipulate data and conduct the exploratory analysis efficiently. EDA is often an initial step in the data analysis process. Data visualization: is the process of transforming observations and findings into comprehensive visual reports and graphics to best communicate those insights to stakeholders who may not have a technical data analyst background. Effectively communicating findings is necessary in facilitating strong decision making–read our tips on how to effectively present data here . Data security and privacy : is crucial preserving the integrity of the data and data analysis process. Data analysts must take measures to protect sensitive data, monitor and control access to authorized personnel, and comply with laws and other relevant data protection regulations. Data analysts contribute to data-driven decision-making and offer valuable insights and recommendations to help companies secure a competitive advantage in their field . The tasks range from technical organization and analytical work to collaboration and communication to non-technical stakeholders. To be successful in their role, analysts must be proficient in data analysis tools and techniques, be detail oriented, and possess strong soft skills to help communicate their findings to relevant stakeholders. Many data analysts today look to improve their skills through data analysis courses or verify their skill sets by securing a data analyst certificate. What is Machine Learning? We hear the term all the time, but what is the definition of machine learning? Machine learning is a subset of artificial intelligence (AI) that utilizes algorithms to break down vast amounts of data . Through the development of these algorithms and models, computers can effectively “learn” and make predictions or decisions without being explicitly programmed. Machine learning essentially supports the design and development of systems that will automatically transform and be improved with the introduction of data or through experience. Unlike traditional programming where a computer scientist writes specific directions for the computer to follow, machine learning is based on the computer’s “learned” conclusions . In other words, computers are trained in hand with large amounts of data and actually learn based on the patterns and relationships found within that data. Machine learning is reliant upon algorithms to analyze data, identify patterns, and build mathematical models based on those patterns. The models created can be used to make predictions or decisions, test hypotheses, or secure comprehensive insights on unseen or future data. Thus, machine learning is proving crucial in expanding the terrain for data analysis and making even stronger organizational decisions. There are three standard machine learning algorithms: Supervised learning: is the process of training a model using labeled data, where the desired output or conclusion is known. The algorithm learns from clear examples in order to make predictions surrounding new, unknown, or unlabeled data. Unsupervised learning: is essentially the opposite of supervised learning. Instead of training a model with labeled examples, the algorithm learns alongside unlabeled data. Its work is to find patterns, similarities, or groupings without a predetermined or predefined outcome. Reinforcement learning : essentially trains an agent to engage a new environment and learn alongside the feedback it receives. The algorithm slowly develops alongside this feedback and adapts its decision-making strategy accordingly, improving its performance over time. Machine learning is quickly becoming a staple in many organization’s data analysis processes and continues to advance and improve company’s ability to test hypotheses and make data-driven decisions. How Does Data Analysis Differ from Machine Learning? In the end, data analysts and machine learning engineers work closely together as their work is both concerned with understanding and exploiting data in order to enhance company decisions. However, the two greatly differ in their objectives and approaches to processing and utilizing data . Data analysis is primarily concerned with interpreting and understanding data with the intention of securing actionable insights, while machine learning focuses on the development of algorithms and models through data so that they can function without human intervention. How Can Machine Learning Help Enhance Data Analysis? In many ways, data analysts and machine learning engineers rely on one another in order to gain a deeper understanding of data. Data analysts carry out the first step of conducting statistical analysis , and from those insights, a machine learning engineer creates models and machine learning systems that scale data, test hypotheses, and ultimately extract deeper insights from data. Through these advanced techniques and capabilities, machine learning complements and enhances the data analysis process in the following ways: Recognizing patterns: through data exploration, data visualization, and data mining, data analysts can identify patterns and generate hypotheses. Machine learning aids data analysts in the face of increasingly large and complex data sets. Through the application of machine learning algorithms, data analysts ensure a more comprehensive understanding of the underlying patterns and trends in their data. Predictive analytics: machine learning models can be trained to more accurate predictions based on historical data. Through the models created, data analysis can offer a sharper analysis on what the future holds, supporting businesses to better mitigate risk, forecast trends and outcomes, and make more proactive decisions. Algorithms and automation: machine learning algorithms help automate the most repetitive data analysis tasks like data cleaning, data preprocessing, and manual data manipulation. Machine learning makes the data analysis process more time efficient and thus gives tech professionals more time to interpret and strengthen their understanding of the data. Detecting anomalies : the first step of data analysis after one obtains data is preparing and cleaning that data such that it’s free of anomalies, errors, or outliers. Machine learning can support detecting and correcting errors, finding and removing outliers, adding missing values, and merging distinct data sets. This is particularly useful in fraud detection, catching faulty machinery, or in identifying abnormal consumer patterns. Communicating findings : machine learning aids data analysts provide enhanced data visualization. Machine learning techniques can be integrated with data visualization tools in order to create more dynamic and interactive representations of data. Data segmentation: machine learning is often used to segment data into specific groups based on similarities and patterns identified. From these segments, whether they be customer segments, market segments, or other categories, companies can offer a more personalized experience and optimize everything from marketing campaigns to product design. In many ways, data analysis is the precursor or the complementary step to machine learning. Through integrating machine learning techniques, data analysts can automate repetitive tasks, deepen their understanding of data, use algorithms to test hypotheses that strengthen predictions and help mitigate risk, and finally, lead to stronger recommendations and company decisions. If you’re interested in a job in data analysis or working as a machine learning engineer, then look no further. Ironhack offers bootcamps which will arm you with the basics to kickstart your career, or help you dive deeper into these areas to strengthen and expand your opportunities as a tech professional.

The world of tech is vast and within that wide range, there are a myriad of job positions that intersect and collaborate with one another, forming a network of assistance and support . Two positions that do make contact sometimes are cloud architects and data analysts. You may think: how do these two professionals end up collaborating with one another when one is concerned with the cloud and the other is focused on analyzing data? Good question! But first things first, what are they and what do they entail for the people who do work with the cloud and data? What is Cloud Computing? We have all heard of the cloud, but what else do we know about it, other than that it stores data? The following cloud computing definition from Merriam Webster dictionary may help clear up your doubts: The practice of storing regularly used computer data on multiple servers that can be accessed through the Internet. The keywords here are storing , multiple servers , and accessed through the internet . The cloud is used for storage, management, and processing of data that does not depend on a local server or personal computer. Instead, a slew of cloud service providers such as Amazon Web Services, Microsoft Azure, Google Cloud, or iCloud provide users infrastructure as a service (IaaS), platform as a storage (PaaS), and software as a service (SaaS), which provide the following functions: IaaS: because users are given access to virtual machines, networks and storage,  they can create their own computing environment. PaaS: this service is best for developers and can be used to test, build, modify and open applications with no fear of affecting the previously established infrastructure. SaaS: users can access software instantly over the internet, removing the need to install or update anything. To access any cloud storage, the internet is a must since it’s used as the connection between your device and the network of remote servers that house all of your data. Cloud computing services require payment to store data on their private servers because they are providing an essential service to their customers. The benefits of cloud computing cannot be understated, considering the cloud gives users the ability to access storage from anywhere as long as they have access to the internet. Not only does it increase accessibility for completing tasks as high up as on a plane crossing the Atlantic, but it also provides a reliable and strong infrastructure for your data storage needs. What is Data Analytics? The importance and influence of data analytics is not only in the tech world, but also in the political, financial, and business realms: where there’s data, there’s knowledge, and knowledge is power . Although data analysis sounds like it would be simple, it is a complex process that includes many steps to achieve their final results: Main objective: defining the main objective of the data collection and analysis paves the way for the rest of the process. Data collection: you must collect the data that you want to analyze, which can sometimes be a large pool of information. Cleaning and preparation: the data must be sifted through, evaluated for viability, and prepared for the analysis, since there may be inconsistencies and outliers that impact the data. Exploratory data analysis: this first encounter with the data will give the data analyst  first impressions of the content, patterns, outliers and other potential results. Data analysts create visual representations and follow leads on patterns that may appear when exploring this data. Data modeling and analysis: this would be the meat and potatoes of data analytics; data analysts employ their data analysis tools and strategies to dig for any insights and answer any questions that they had upon setting their initial main objective. These techniques may include regression modeling, factor analysis, time series analysis, clustering, and classification. Interpretation and insight generation: the collected data has gone through various analytical techniques and now insights can be determined and questions answered. Analysts also identify any trends, patterns, relationships, and other important information that could provide them with more useful details. Data visualization and reporting: by using all the information from the data, data analysts create visual representations that show everything in an easily understood manner as well as reports that detail the complete data analytics results. Going through this process, data analysts will be able to provide their insights and other important information to help their company make any necessary adjustments or improvements. Although the higher-ups make the final decisions upon seeing the data, the data analytics team has a lot of influence and power to make big changes to their products and the future of their company. Due to the importance that companies place on the trends and insights that they provide, data analysis jobs are in high demand . What Responsibilities do Cloud Architects Have? Although they have the word architect in their name, cloud architects don’t have anything to do with designing houses; however, they do create, manage, and handle the cloud infrastructure at companies. Upon arrival at a company, they frequently establish the cloud network for their offices. Cloud architects are also responsible for optimizing the company’s use of the cloud computing system and carrying out cloud-based solutions in the case of any issues by using the following: Front end platform: all of what the client interacts with includes the front end cloud infrastructure and when clients will use this software to gain access to the cloud. Back end platform: the back end cloud infrastructure is the hardware and software with which clients don’t directly interact. Servers, network devices, storage, hypervisors, and virtual machines are examples of components of back end cloud infrastructure. Cloud based data delivery models: the three main data delivery models that are provided to companies are the three mentioned above: IaaS, SaaS, and PaaS. Since there aren’t many degree programs dedicated to cloud computing, many cloud architects will study other degrees first then sign up for a cloud certification program where they will acquire expertise on programming, cloud infrastructure, and cloud computing security. There are many cloud computing jobs available right now since the reliance on the cloud is growing exponentially. How do Cloud Computing and Data Analytics Differ? These two tech fields don’t have much in common in terms of their purposes, but they are both important aspects of any company. Cloud computing provides a company with a large amount of storage space, access to online applications, and a testing ground for development. Nevertheless, once the main cloud infrastructure is established, maintenance and optimization are at the forefront . Cloud architects have many responsibilities but since they are solely focused on the cloud, they do not have a high project turnover rate . Data analytics is not a static process like cloud computing is; data analysts choose a main objective that they focus on to generate the data-driven results and they go from goal to goal, collaborating on diverse projects. Once one set of questions has been answered or insights have been achieved, they can move onto the next goal or query . This gives data analytics a dynamic feel in comparison to cloud computing: there will always be the next new question to ask or insight to figure out. How do Cloud Architects and Data Analysts Work Together? Can you guess what brings these two professionals together? If you guessed data , then you are correct. Both deal with huge amounts of data daily so their paths cross when there are potential data or storage issues. Cloud architects are in charge of handling the storage of all of the company’s data and in the case of any issue, they need to troubleshoot and get to the bottom of the problem. Data analysts depend on having access to cloud storage since they are working with the data for a potentially extended period of time. They may need to discuss the amount of space they need, speed of the connection, or availability of space amongst other questions. Although cloud computing and data analysis are more different than similar, data analysts do rely on them for their data storage needs, which makes this relationship an important one at the office. In the tech world, a career in either one of them is highly desirable and as more offices go remote and ditch physical servers and computer space for cloud storage space , the need grows for both of them. At Ironhack, you can find our Data Analytics Bootcamp to become a data analyst. Check it out today!

I mean, why not?! Madrid and Barcelona, two of Europe’s most renowned cities, are incredible cities for practically any purpose. But when it comes to taking a tech bootcamp, you truly can’t go wrong in these two sunny destinations. They each have their specificities, of course, and Spain itself is a bustling place for emerging tech professionals. In this article, we’ll cover the benefits of both cities, in addition to what Spain offers techies. The Tech Scene in Spain Most people would consider Spain to be an average European country when it comes to technical advances, falling behind nations like the UK and Germany. But in reality, Spain ranked seventh on the Digital Economy and Society Index (DESI)’s 2022 ranking with the following scores in these four categories: Human capital : when it comes to the knowledge and experience of the Spanish people, the country’s average is below that of the EU; this contributes to the skills gap and overall slow growth. Connectivity : considered a regional leader in the field, Spain ranked third overall in terms of connectivity. Integration of digital technology : while larger companies are able to keep up with changing technologies, smaller and medium-sized enterprises (SMEs) are falling behind, contributing to the growing skills gap. Digital public services : Spain ranks high here, offering a range of digital services in different sectors. The tech sector makes up 19% of Spain’s GDP and following the COVID-19 pandemic, the following three points were highlighted as the main focuses : the digitalization of industry and business, the digitalization of R&D+i, and improving the population’s digital skills. And when it comes to startups, the vast majority of one of Europe’s biggest startup hubs are located in Madrid or Barcelona. The workplace gap in Spain 2021 witnessed 21% more tech employees join the market, but there’s still a long way to go to fill the 100,000+ vacancies of 2022. Where does this gap come from? Why don’t people just choose to join the tech sector? Let’s discuss three main reasons: Outdated university curriculum: after four or five years of university studies, graduates don’t have experience with the newest skills in the market--which is exactly what employers are demanding. What they learned was important when their syllabus was created four years prior, but lacks the newer technologies that are in such high demand today. Accelerated digital transformation: technology is moving so fast and professionals are simply unable to keep up with the newest innovations. Weakened on-the-job development : reskilling or upskilling would be a great option for employees looking to transfer their current skills to new and in-demand skills, but many companies lack the resources to properly train their current employees. These three factors, although significant, aren’t insurmountable. In fact, we have two specific areas that can help the aforementioned barriers to tech: education and business. Tech education in Spain In 2020, only 6% of university students were enrolled in a tech-related course. And for these students that are studying tech, they’re learning the current technologies from when they began their studies, which are outdated and not in demand once they are ready to enter the workforce. Here’s what’s being done to improve this: The Spanish government introduced Formación Profesional , a two-year course designed for those looking to learn in-demand skills in an intensive manner. 53% of people recently hired in tech have come from these bootcamps. Tech-focused businesses in Spain Companies get their next generation of techies from universities, right? So it makes total sense that companies and universities should work together to ensure that students are learning what companies are looking for in future employees. Universities that partner with bootcamps or universities can help create a syllabus that truly prepares students for the workforce and the skills they truly need. Mentorship programs between professionals and students can also help students get a feel for what the professional world of tech is like. Sector-specific Tech Trends in Spain Before we head into the specificities of Madrid and Barcelona as cities, we want to highlight the future of some sectors within tech: web development, UX/UI design, data analytics, and cybersecurity. Web development in Spain Web development is rapidly growing in Spain, with 94% of Spaniards using the internet daily. This increase in usage means demand for tech professionals, especially for software developers in Madrid, has skyrocketed. All companies need web development experts to help maximize their online presence. We can expect the following in the near future: An increase of progressive web app usage The growing popularity of 5G networks Increased use of low-code/no-code platforms The introduction of cross-platform app development UX/UI design in Spain UX/UI design was ranked as one of the top 10 most demanded skills in 2022, but that’s not all: experts predict that 100 million UX/UI professionals will be needed by 2050. Let’s keep these points in mind: UX/UI design is an incredibly broad field with roles for practically anyone Salaries and job responsibilities will vary according to the role Successful UX/UI designers boast a varied skill set, with both hard and soft skills Data Analytics in Spain When it comes to data, Spain ranks 13% above the EU average and boasts advanced data strategies, mature platforms, and active methodology for measuring the impact of open data. To maintain its position as a data leader, up-and-coming data professionals will need: To be experts on the entire data analytics process, including collection and processing To be able to count on real time or live data To use third-party data or the cloud to alleviate some of the weight of storing so much data Cybersecurity in Spain We’ve said it before and we’ll say it again: improvements in cybersecurity technologies means that cyber attacks are also getting better. Spain ranked fourth in the 2022 Global Cybersecurity Index, but will need to keep the following points in mind as cyber attacks become more advanced and Spaniards put more and more of their data online: Spain and the US experienced the most malware attacks during the pandemic and 91.8% of those were successful More than half of the Spanish population is concerned about their personal data The country is starting to adopt federal initiatives to improve overall cybersecurity Now that you know why Spain is a great place to dive into tech, let’s head into more detail about two of the world’s sunniest cities: Madrid and Barcelona. Tech in Madrid Choosing to jumpstart your tech career in this Spanish capital is a great choice; with incredible food, a low cost of living when compared to other European cities, and a great mix of city and rural living, Madrid truly has everything you need. Still looking to be convinced? Here’s some more things to keep in mind: Despite being a Spanish speaking country, the vast majority of both native Madrileños and visitors speak in English–Spanish is also easy to pick up. You’ll be ordering your caña in no time! As the capital, lots of both Spanish and European tech companies have offices or headquarters in the city, setting you up for easy networking. The city boasts an incredible quality of life with great public transportation, tasty food, and overall positive vibe. You’ll never run out of things to do! When you take a study break, you can head to one of Madrid's many museums or churches, or even take your computer to one of its many parks and study there! No matter what you like to do, Madrid has it. With a vibrant student and expat population, you’ll find locals and foreigners alike around every corner, helping you acclimate to Spanish culture while getting a little taste of home. Tech in Barcelona Very few cities can offer the incredible combination of beach and city, but that’s exactly what Barcelona does. Located in the Spanish state of Cataluña, Barcelona is just a few hours from France and other Spanish cities and offers close proximity to some of Europe’s leading tech companies. Take a look at what else Barcelona offers: This incredibly multicultural city mixes Spanish, French, and Catalonian culture together to create a unique and tasty local cuisine. You’ll have the chance to learn both Spanish (Castellano) and Catalán, while still being surrounded by English speakers and expats from all over who flock to the picturesque streets of Barcelona. Its border to France means that it’s in a great central location and links to the rest of Europe, boasting offices for some of the continent’s leading companies. You can enjoy relatively temperate weather year round, enjoying the beaches and many parks during the majority of the year. Ironhack in Spain We realize the potential of both Madrid and Barcelona and Spain as a whole and that’s why we were the first ever bootcamp in Spain, established in Barcelona. Since then, we’ve expanded our campus to Madrid, where we offer a wide range of options so that your bootcamp experience is the best it can be: in-person, remote, full-time, or part-time courses in web development, UX/UI design, data analytics, and cybersecurity. Both our campuses, located in Matadero in Madrid and Poblenou in Barcelona, were designed with our students in mind, offering nearby public transportation options and warm and welcoming classrooms. Does Spain seem like the perfect place to kickstart your career in tech?! We think so! Choose between Madrid and Barcelona, or choose remote, and let yourself become the next techie to join the tech sector in Spain. You won’t regret it.

Two growing and essential fields in the tech world are artificial intelligence and data analysis, but what are they and how are artificial intelligence engineers and data analysts both similar and different? Let’s dive in: What is Artificial Intelligence? Artificial intelligence isn’t anything new; many of your minds may go straight to the recent birth of ChatGPT or even the Terminator films from 40 years ago when you hear the word, but it’s important to keep in mind that it’s not simply the ability for machines to act as if they were human. According to the Merriam Webster dictionary , the artificial intelligence definition is twofold: a branch of computer science dealing with the simulation of intelligent behavior in computers the capability of a machine to imitate intelligent human behavior So what does intelligent human behavior entail? Artificial intelligence refers to software and algorithms that collect and analyze data to solve complex problems that typically require a human-level of intelligence and just like humans, artificial intelligence technology depends on several methods to take in as much information as possible to make the best possible choice. Some of the most useful methodologies and algorithms that they use include: Machine learning Deep learning Natural language processing Rule-based systems Machine vision Through employing a combination of these methodologies, artificial intelligence is capable of both learning and evaluating the collected data to arrive at the most appropriate and efficient solutions to the problem and as a result, it is a valuable tool in the tech world. Artificial intelligence as a field of study is growing more as technology becomes more complex and more machines, software, and other programs incorporate AI. There are many job positions that work with AI such as artificial intelligence engineers, machine learning engineers, data engineers, robotics engineers, and software engineers. Since it’s such an impressive and efficient tool, companies and businesses want to harness its power to perform a variety of functions that support and maximize their reach, efficiency, and potential. Artificial intelligence examples There are currently a vast number of technologies and applications that already use AI to complete their tasks such as the following: Virtual assistant software: have you ever asked Siri a question? Siri, Alexa, Google Assistant, and other virtual assistant programs use algorithms and language processing software to provide users with whatever information they need in an instant. Chatbots: on many websites, you will immediately encounter a pop up on your screen that asks you if you need any assistance with their product or have any questions. This artificial intelligence chatbot software uses machine-learning techniques and language processing to imitate human behavior when answering your questions. Image recognition: facial recognition, autonomous images, and object detection rely on the identification and analysis of people, objects, and scenes in images that this type of artificial intelligence provides. Recommendation systems: that ad on Instagram that’s always exactly the same for months on end wasn’t chosen by you; it was chosen by AI to appeal to you based on all the data of which these social networks can access and leverage. Companies employ this type of AI to choose the products, media, and content that they believe will most interest you. Financial trading: increasing money flow in your bank account can be quite difficult but AI has the power to analyze data and projections and detect fraud so that your finances will be handled smartly and traded swiftly. Many more examples of artificial intelligence can be found throughout our modern world and it's important to realize that every day you’re interacting with them on social networks, websites, or applications. Artificial intelligence is all around us! What is Data Analytics? Data analytics as a field sounds simple, but it includes several steps that focus on not only gathering and organizing a large amount of information, but also evaluating, confirming, and interpreting all of it. Once information has been collected and analyzed, decisions need to be made based on the story, insights, and patterns that the data is presenting. Companies, organizations, and other entities can use the insights summarized in the data in a variety of ways such as businesses looking at buyer tendencies to generate feedback for their products or evaluate the reviews that their products receive. The power of data cannot be underestimated and knowing this, businesses can leverage it to help them make important decisions. Data analysts depend on a few different techniques to complete their analysis: Regression analysis Factor analysis Data visualization Monte Carlo simulations Data mining Time series analysis Hypothesis testing Cohort analysis Employing these and other techniques, data analysts generate insights and patterns that guide businesses to decide the best course of action and as a result, the companies with the best data analytics teams can make the most informed decisions for their future. Companies know that to beat the competition, they must focus on the data and have more accurate predictions; having top notch data analysts makes a huge difference . What Responsibilities Do Artificial Intelligence Engineers Have? For artificial intelligence to be created, maintained, and improved upon, AI professionals have to carry out many tasks, designing a network of several algorithms to create the illusion of human-like behavior ; the process requires them to have expertise in fields like software development, programming, data science, and data engineering. Although artificial intelligence engineering is related to these fields, AI engineers seldom write code ; they do, however, search for and manipulate data, develop and test machine learning prototypes, and use the API to create and design artificial intelligence software or applications. Some other responsibilities also may include: Organizing and providing structure for the development of artificial intelligence systems Carrying out statistical analysis and interpret the data to improve the decision-making process Streamlining artificial intelligence systems for data scientists Coordinating with other teams that use artificial intelligence-based software and technologies How Do Artificial Intelligence and Data Analysis Differ? Artificial intelligence has a wider scope that focuses on developing systems that make the final decisions, but data analysis is a subset of artificial intelligence that can only inform others in the decision-making process. Artificial intelligence uses data analysis as one of its techniques to achieve its human-like behavior and also has access to other of the techniques mentioned previously like machine learning and natural language processing. Apart from having a wider scope, artificial intelligence also doesn’t require human intervention when the final decision needs to be made; with data analytics, insights, feedback, and patterns are presented to people so that they can make an informed decision. How Do Data Analysts and Artificial Intelligence Engineers Work Together? Since data analysts are focused on providing insights into products such as for an artificial intelligence model, they do collaborate with AI engineers on their projects. Their goal is the same: improve the product experience but they complete their objective in different ways . As we mentioned above, AI engineers focus on creating, maintaining, and improving their AI models and to carry out their projects, they may depend on data analysts for feedback. Data analysts on the other hand, are supporting AI engineers in gathering data on their project and looking at what they can do to assist them in creating the best product possible. Throughout the artificial intelligence creation process, data analysts provide parameters and guidance to show AI engineers where to go with their product or what modifications to make based on their own data analysis. Data is powerful and AI engineers can use its insight to weave algorithms into a complex network of art. Although data analysis and artificial intelligence seem to both be complex sciences that depend on various algorithms and computer technologies, they have diverse functions and applications. We have seen that data analysis is an important tool that provides the information, insights, and tendencies yet has its limit unlike artificial intelligence; by using data, artificial intelligence has the power to make a final decision without human intervention . Both are helping companies and businesses grow more through their distinct roles as company tools to connect more with the users and clients that interact with them. Their significance in the tech world should not be downplayed and if working with either of these two disciplines interests you, Ironhack provides courses to become an expert on either. Now’s your chance to check them out below.

At Ironhack, we’re fully committed to providing our students with the latest tools necessary to excel in the tech industry and that means one thing: constantly updating our syllabus and curriculum to reflect market changes and incorporate any new trends. And that’s just what we’ve done: added artificial intelligence and large language models to our Data Analytics Bootcamp. Why? Well, mainly for the reason that we just stated, but also because: We believe in the bootcamp model because we know that the tech industry moves so fast and university graduates simply aren’t equipped with knowledge of the latest tools post-graduation. Our Career Services works closely with industry HR experts and has seen a growing demand for proficiency with artificial intelligence and large language models from hiring managers. Artificial intelligence and large language models have just begun to take off and we know that they will continue to evolve, meaning any tech professional of the future will need to know how to master these to land their dream role. And while ChatGPT has been all the rage over the last few months and you’re dying to learn how to use it professionally, there are lots of artificial intelligence tools and large language models that can help automate daily tasks, freeing up your time for more interesting duties, in addition to things never before seen, such as processing large amounts of data or displaying said data in a visually appealing way. Before we dive into artificial intelligence and large language models in data analysis and specifically how we’ve incorporated them into our bootcamp, let’s define them and give a few examples. Artificial Intelligence and Large Language Models We know you’ve heard of these words, but could you actually define what they are?! We’ll help: Artificial intelligence : this field is dedicated to teaching a computer to perform tasks that normally require human intelligence. Regenerative AI: as the name suggests, regenerative AI focuses on developing systems that can repair themselves, like a human heals a scrape. Generative AI : this new and in-fashion field creates new content, such as text, images, or video (ChatGPT is a great example of generative AI!). Natural language processing (NLP): this subfield of AI works to process and understand the human language so that it can recreate it in automated tasks. Large language model : LLMs are a type of generative AI where most innovation has taken place over the past few years; it’s been trained on large quantities of text using self-supervised learning. ChatGPT has been making a lot of noise over recent months and there’s a reason why: its ability to create humorous and human-like responses to a wide variety of requests is completely revolutionary in the tech world. But in case its servers are full or you’re interested in seeing what else is out there, we’ve collected a few other AI tools that could be just what you’re looking for: Google Bard : Google developed Bard, a conversational AI tool; differently from ChatGPT, however, Bard searches the internet in real-time to generate answers about any topic with no recency restrictions. Bing with AI : this tool is designed to be used with Microsoft’s search engine, Bing, and also uses current data to provide answers to your questions. DALL-E: looking for a more visual output? DALL-E is able to generate images using AI; in just a few moments, you’ll have the image of whatever you request. Soundraw : what about an audio-focused approach? Soundraw allows you to take any text and create the music of your choice. The best part? You own the content you create in the tool. No matter what tool you choose, familiarity with artificial intelligence and large language models will help your data analytics career skyrocket, opening you up to lots of new opportunities. Learning AI & LLM Tools for Data Analytics with Ironhack One of the most time-consuming tasks that data analysts deal with daily is processing and analyzing large amounts of data. However, AI & LLM tools can transform the way data analysts work, making their jobs easier. Here’s how: These tools can make it easier for data analysts to process, explore, communicate, and collaborate on their data-driven insights , making future decisions clear. Tools’ abilities to use natural language can help data analysts communicate their findings to non-techie stakeholders in a way that they can actually understand. These tools can also look for trends or patterns in the data that was otherwise unrecognizable to the human eye, helping draw new insights. As these tools are so new, there’s an increasingly high demand for professionals that are not only skilled in data analytics, but also in AI and LLM tools. After all, no professional who’s currently working in the field can boast certifications or education in such tools and those who are in a traditional university setting are stuck with predetermined curricula that don't cover the latest trends. Ironhack is unique in providing students with the education they need to enter the job hunt with these skills on their resume– a perk specific to Ironhackers. Can anyone learn ChatGPT? You might be thinking that such a cool and advanced tool means it’s hard to learn and it will be impossible to jump into data analytics so quickly. However, the opposite is true; data analytics isn’t your typical tech job that requires just coding. Successful data professionals are skilled in statistical analysis and mathematics, but they also must be good communicators, logical thinkers, and successful problem solvers. ChatGPT and other similar AI and LLM are facilitating the more data-heavy and numbers-driven side of data analytics and allowing data professionals to focus on the results, interpreting the data, and presenting it to colleagues, opening the door for more and more people to join the field (which is good–lots of data professional roles go unfilled yearly!). There’s truly never been a better time to jump into data analytics and there’s no better place than at Ironhack. Check our Data Analytics Bootcamp out today and start your journey into the world of tech.

You’ve heard about A/B testing and it makes sense, right? T esting two options to see which one works best seems like a great way to make decisions and that’s exactly what UX/UI designers do when making a final decision on a design. Or maybe you haven’t heard about A/B testing and are a little confused with what it means and how it can be of use in UX/UI design. No worries: stick with us and we’ll outline what A/B testing is, the purpose it can serve, and how to harness its potential in the field of UX/UI design. What is A/B Testing? To put it simply, A/B testing is when you compare two different solutions side-by-side to discover which one works best with your desired parameters. It can also be called split testing or bucket testing and is a very popular strategy in tech. It can be used to see which web page element, font size, image, or web design is more visually appealing, but it can also be launched to see to which the users better respond. To understand the need for A/B testing, an important pillar of marketing needs to be mentioned: all customers are different and their behaviors are determined by their surroundings. Even in completely identical situations, customers can make random decisions that affect companies differently; there’s no perfect way to predict customer decision-making, of course, but A/B testing can provide you with real life examples of how customers react to two different options. Benefits of A/B testing in UX/UI design As a field where visual designs can define the success of a campaign, UX/UI designers frequently employ A/B testing as a helpful tool to narrow down their decision-making process. And when A/B testing is used correctly, it can lead to: Increased web traffic : with better titles and headings for your content, you’ll be able to attract more visitors to your page. Higher conversion rates: finding out that customers are more likely to click your CTA when it’s green and has a visual icon means that you’ll convert more and more visitors into clients. Lower bounce rate : are customers quickly leaving your website or a certain page? By changing small factors one by one, you can determine the cause of their page abandonment and work to improve it. Let’s dive right into A/B testing and give some examples, key rules, and what to look for upon finishing the experiment. Keys to A/B testing in UX/UI design It might be tempting to test two completely different designs to see which works best, but A/B testing actually gives you the best results when you focus on a very specific metric . It sounds like a lot of work for a little change, but all marketers know that the smallest adjustment can bring around significant results. Here are some of the keys to A/B testing to keep in mind when planning your own experiment: Figure out problem areas: this is an obvious first step! Your A/B tests shouldn’t be random aspects of the website that you think can be improved; you should try to see what isn’t working for customers, update the pages that are receiving the least amount of page views, and listen to customer feedback. Once you know why customers aren’t buying your product or service or even staying on the page long enough to explore, you’ll have a better idea of what you can fix. Remember each company and customer is unique : what works for another company won’t work for you, no matter how similar you think the two are. When deciding on room for improvement, check out what competitors do, but don’t let yourself get carried away trying to copy them. A green search CTA button might be getting lots of traction on your competitor's website because the backdrop is a simple yellow; if you add a green CTA on your blue background, it may get lost. Make smart, data-driven decisions : we know there are lots of things you’d like to improve, but you probably don’t have the time nor the resources to do all of them. When it comes to figuring out the precise areas where you want to run an A/B test, make sure you use data to figure out problem areas, such as the pages with the lowest page visits or conversion rates. This will help you target the exact areas that will have a bigger impact on your success. Define your goals and track your results : the most effective A/B tests have a clearly identified goal, helping you make sense of the results and turn them into actionable insights for your company. And as you start receiving data, make sure you record the information so that it can be of use in the future. Examples of A/B Testing in UX/UI Design It might be hard to fully understand what we mean by A/B testing, so let’s give you some examples so that you can apply them to your own tests. Button placement: does a CTA work best at the top of the page or the bottom? Some of your team may think that it’s best to have it at the bottom, once the user has finished consuming all the information; others may think that having it as the first thing users see is better. To test which works best for your website, you would create two separate designs and place the CTA at the bottom on one and on the top on the other. Then offer each option to different groups of users, hopefully of equal numbers, and then see how each performs. Subject lines in emails : are users more likely to open an email that has their name in the subject line? Or is it best to keep it generic? For your upcoming email campaigns, half your customers will receive an email with their name and half without; once the campaign is over, you can see which email had more clicks and overall success. Logo design : is a yellow logo too similar to another design or a red one impossible to read? With A/B testing, random groups of clients will encounter each design option and once the test is over, you’ll be able to pick the one to which clients best react. We could go on and on–after all, customer decision-making is constantly changing and new factors are affecting the way they shop and consume content online. That’s why A/B testing’s role in UX/UI design specifically is set in stone; any up and coming UX/UI designer needs to be familiar with A/B testing and ready to employ it in their next project. If you’re interested in UX/UI design and what else UX/UI designers need to keep in mind, Ironhack’s UX/UI Design Bootcamp is expertly planned to teach you exactly what you need to know to excel in the field. Interested? Check out our course here:

The Importance of Data Quality in AnalysisYou know data analysis is important; in today’s day and age, how could you not?! Everywhere you turn, you’re being asked about your own data, giving companies permission to use it, or even using it yourself to make better decisions. And while data analysis is quite useful, there’s an important factor to keep in mind: data analysis is useless without clean and high-quality data. We know what you’re probably thinking: data is data! Any kind of data can be very useful when it comes to making decisions. Well, bad news: there are lots of things to be cautious of when using data to make decisions and when analyzing your data from the start. Data can be misleading, altered, or even completely wrong. How? Why?! We’ll cover these questions and explain the importance of quality data further in this article. What is Data? Understanding the importance of quality data isn’t the first nor second step to ensuring you’re running a solid data analysis. Before you begin, understanding data itself is key. Data is information that has been gathered that, on its own, doesn’t carry any specific weight or meaning. However, once data has been processed or moved, it can lead to highly valuable insights. Data isn’t just numbers; data comes in the form of text, numbers, graphs, symbols, images, and much more. Generally speaking, data is split into three categories: Numerical data : this kind of quantitative data includes any sort of number, such as times, weights, height, volume, and more. Categorical data : characteristics and identifying information, such as gender, race, or marital status fall into categorical data, which is quantitative. Ordinal data : bringing together these two types of data, ordinal data consists of both categorical and numeric data, such as a 1 to 5 review form that allows customers to also leave written comments. We know you’ve heard people talk about data constantly; you’ve probably heard it so much that you’re actually unsure of the exact uses of data! Data is incredibly important, however, and greatly affects the following five areas: Decision making : data-based decision making is an absolutely crucial part of business! And there’s no better way to make a decision than by using data to back up your choices. Data can help you defend your choices and guide you towards smarter decisions. Problem solving : when something that seems random and unexplainable happens, what can you do?! Use data, of course! Data can help show you why something happened and get to the root of the issue. Understanding information : Good decisions are data-backed, but even better ones are fully understood. Instead of just seeing the outcome, you can use data to see what occurred during each and every step of the process. Improving processes : by seeing what’s working and areas for improvement, you can improve productivity, highlight problem areas, and help to streamline business operations. Understanding customers: at the end of the day, you want to create a product or service that customers want. To do this, you need to understand their thinking and what makes them either buy a product or decide to choose a competitor. Now that we’ve defined data and why it’s important, let’s dive right into the good stuff: quality data. What is Data Quality? All data comes from our records or client information, so it must all be accurate and helpful, right?! Unfortunately, that’s not the case. Factors like outdated client information or missed deadlines can lead to the following problems: poor customer relations, inaccurate analytics, bad decisions, wasted money, and overall lower business performance. On a high level, companies that use high quality data perform better, thanks to more accurate measures. Data quality vs. data integrity As someone interested in data, you’ve probably heard of data integrity, which lots use interchangeably. While similar, the two differ: data quality is just one part of data integrity, which focuses on three additional areas: data integration, location intelligence, and data enrichment. Data integration highlights the need for all data, regardless of its origin, to be integrated properly into systems to provide for a quality analysis. When you add location intelligence to your data analysis, you’re adding even more details--details that can provide more insight. Finally, enriching your data means that you are providing even more information, such as consumer or business details, that contribute to a more complete final picture. What makes data high quality? There are lots of factors that make data high or low quality, but let’s dive into five: Accuracy : is the data you’re looking at true? Is it free of bias? Inaccurate data can lead to incorrectly drawn conclusions. Completeness : data that’s missing from within a dataset can offset the final outcome; ensuring that your dataset is complete means that all fields are filled for all products, allowing you to get an accurate picture of the company’s status. Reliability : your data needs to be consistent throughout your entire company; clients with contradicting information can make your data analysis faulty. Relevance : is this piece of data important to your final conclusions? Wasting your time analyzing random data points can not only use up valuable resources, but also cloud the truly important analysis. Timeliness : ensuring that data is recent and up-to-date helps guarantee that the information is accurate, avoiding problems down the line. Be aware of “dirty data” So now that you know what kind of data is good, let’s explore the other side: the dirty data. This kind of data can be harmful to your overall company performance: Insecure data This shouldn’t be a surprise! Data regulations are popping up right, left, and center in practically every corner of the globe and it’s the responsibility of the company to ensure that their data storage and collection is lawful. And for companies that operate internationally, guaranteeing that the data meets every guideline, no matter their home location, can be quite the challenge. Meeting these regulations, however, is absolutely essential. Companies that ignore these rules can be heavily fined, in addition to suffering reputational consequences. Our tip: stay up-to-date with local regulations and ensure that you clean and check your data frequently to make sure you’re not holding onto old data. Inconsistent data One of the biggest challenges in analyzing data is collecting information from across multiple departments. What if the sales team has a client’s email listed one way and the finance department has it another way? Or a client shows up twice in the database, therefore providing inaccurate results? Streamlining the data analysis and management process can help you avoid inconsistency issues. Our tip: use a data management tool that can be universally employed throughout your company, eliminating contradictory information and duplicate accounts. Incomplete data Imagine you’re trying to determine the average age of your customers at a certain location, but realize you’re missing age information for more than half of your database? Or you’re trying to see if men or women are more likely to buy your product, but don’t have complete gender details for all clients? These gaps in your data may seem minor when looking at just one client, but can seriously skew your results upon analysis. Our tip: search your database to find missing fields, pull these clients and fill these gaps, and focus on obtaining this information for all clients in the future. It seems tedious, but will benefit you in the long run. Real Life Examples of Data Analysis Using Quality Data Now that you have a good picture of what quality data is, how it affects quality analysis, and the dangers of dirty data, let’s dive right into some real-life examples of how quality data can have a positive impact: Medical care : through global databases that have basic patient information, such as age, gender, symptoms, and diagnosis, but no further identifying information, doctors can use the decisions and records of past patients to compare their current patient’s status, see what’s worked in the past, and make the best possible decision for their treatment. Ensuring that they’re working with only the cleanest data sets is absolutely essential for patient wellbeing. Recommendations : one of the data’s best uses is to see what customers like, what they don’t like, and what they’re searching for. With this information, product teams can create in-demand products or services that are desired by the customer; they can also avoid creating products that customers won’t buy, therefore saving time and resources. Missing information or gaps in the data can lead to faulty recommendations and clients can be more likely to use another service that aligns better with their likes and dislikes. Insurance : through collected data, insurance companies can offer clients more accurate and better deals, in addition to using the data as a reward; drivers with a good driving record can benefit from additional perks. Inaccurate data can cost the insurance company if they get the offer a lower premium than what the client really needs. You get it, right? Quality data is absolutely crucial when it comes to data analytics and if your dataset is outdated, missing information, or contains duplicate data, your final analysis could be completely off. At Ironhack, we know the importance of clean and quality data --in fact, that’s why we offer our Data Analytics Bootcamp to prepare the next generation of data analysts. Interested? Here’s what you need to know.

You’ve decided front-end web development is for you; you’d love to focus on the client-side of web development, creating intuitive and efficient interfaces that facilitate client use . And you probably already know this, but both HTML and CSS are absolute necessities for any front-end developer. What are they? How do they differ? Are both really necessary? How can you learn them? We’ll discuss this and much more in this article. Why are HTML and CSS Important? We see this question a lot and we get it! Especially for beginners, there seems to be a massive number of programming languages out there and tools you need to know. But HTML and CSS are truly essential to your work as a front-end web developer. In fact, HTML and CSS knowledge is so valuable that it’s highly desired in other roles as well. Imagine that you’re working as a freelance makeup artist. Instead of having to hire a web designer to build your website/app and hire them to deal with every single issue, you’ll be able to do it yourself, saving valuable time and resources. Regardless of your area of work, today’s world is so digitized that such knowledge is absolutely crucial to your success. As you continue reading our article, think about HTML and CSS applications that could be useful in your professional or personal lives. It’s probably worth learning! What is HTML? Short for HyperText Markup Language, HTML is the generic language used to create web pages, describing the structure of the site. Using tags and hidden keywords, HTML tells the site what to display, but is not a programming language; HTML is a markup language. Through specific keys, HTML dictates the format, style, font size, and pictures that are seen on a page. Within an HTML page, there are five sections: <!DOCTYPE>: this tag exists to tell the browser about the type of HTML used on the page; it doesn’t contain any content. <html>: there must be a <html> tag at the beginning and end of each document, communicating to the webpage that anything between those tags has HTML content. <head> : this section contains metadata; this part isn’t displayed when the page loads but instead contains key information about the page, such as characters, links, and styles. <title> : this is where the title of a webpage goes; this is a key place to include keywords if the web page wants to be well-positioned in search engines. With only one <title> tag per page, it’s a key part of the page setup. <body> : here’s where the meat of the website lies, containing all text, paragraphs, links, images, videos, and more. Here’s an example of what HTML tags would look like: <!DOCTYPE html> < html > < head > < title >Ironhack</ title > </ head > < body > < h1 >Ironhack</ h1 > < h2 >Ironhack</ h2 > < h3 >Ironhack</ h3 > </ body > </ html > Sounds good? Let’s dive right into the benefits of HTML and how it can help you as a web developer. Benefits of HTML HTML is widely used and there’s a good reason for that! It’s quite easy to understand and implement, in addition to being free and easily accessible to all. In addition, HTML offers: Compatibility with any browser Ease of editing Simple integration with other languages Quick speed Small learning curve How to Learn HTML If we’ve convinced you that HTML is just what you need to learn (and as a front-end developer, it is!), you’re probably wondering how to do it. As one of the easiest languages to learn, it’s possible to do it on your own with some practice. But if you’re unsure of where to start, try some of these tips: Online resources : before you commit to paid resources, check out free ones online, such as videos or quick courses. Watching other people walk you through the basics can be extremely helpful. Books : depending on your preferred learning methods, HTML can be best understood through a bit of theory. If you learn better once you have a strong foundation, consider checking out some books on the topic. Bootcamps : once you’ve decided that you really want to dive into HTML and become an expert, there’s no better way to learn than taking a bootcamp. Bootcamps will help you learn the foundational details that are needed to succeed and even provide you with career assistance to help you land that new role. Well, we’ve covered HTML and everything you need to know. Let’s head into CSS so you’re well-versed in both. What is CSS? Cascading Style Sheets (CSS) is a stylesheet language for documents written in HTML or XML, telling websites how to present web page information. It is also able to adjust for various formats, such as mobile and desktop. If this sounds similar to HTML, we get it, but they’re totally different: HTML is the content of the web page; CSS is the presentation of that content. Within CSS, there are three styles: Internal CSS : used to style a single page, internal CSS is used with HTML to provide instructions to a specific page. However, adding CSS to HTML can increase the page’s loading time. External CSS : ideal for bigger pages, external CSS links to an external file (as the name suggests!) and is able to edit entire pages at once. As the CSS code is in another document, your HTML code can run faster and is lighter. Inline CSS : used for styling a specific HTML element, inline CSS is used just for single elements; due to its complex nature, it’s not ideal for large amounts of text. Benefits of CSS CSS is widely used, and not just for its abilities to make web pages more visually appealing. Some benefits of using CSS are: Because CSS allows you to use less code to accomplish multiple things, pages are faster and easier to set up. As CSS works to make a more attractive web page, the overall user experience will be superior and users will be more likely to return to the site. CSS code can be replicated across multiple pages, saving time and speed when it comes to both setting up the page and loading later on. CSS is compatible with websites, mobile sites, and more. Just in case we didn’t make it clear, here’s the difference between CSS and HTML: HTML is a markup language for static web page design and CSS is a stylesheet language used to design the actual page design. You might see articles comparing the two, but they exist for completely different functions and aren’t comparable ; used properly, they are both quality options. How to Learn CSS Before you jump into CSS, it’s best to have some familiarity with both HTML and web development in general; it’s quite technical and you’ll struggle if you try to jump right in with little to no experience. However, the technicalities tend to come with more advanced features and you should be able to start practicing after a bit of studying. The following suggestions will help you on your journey to proficiency in CSS: Online communities : the online web development community is widespread and there are tons of beginners out there looking for assistance. This means that you’ll both have access to other people’s troubleshooting and a place to solve your own doubts. Bootcamps : if you’re looking for hands-on, guided practice where you’ll be able to be corrected and ask questions, bootcamps are probably your best bet. These intensive courses seek to teach you everything you need to know in a short period of time, giving you tons of opportunities to get your hands dirty. Practice : practice makes perfect and you’ll get much better if you practice writing codes and work to solve your own bugs through research/professional guidance. As you get more and more comfortable writing CSS code, you’ll be ready to tackle more advanced tools and even try to learn another programming language. HTML vs. CSS: which is for me? Our answer to this question depends on your specific situation, of course! If you’re working as a front-end web developer, both of these are quite important. Here’s our recommendation: Start with HTML and make sure you understand the foundations of web development; practice creating very basic web templates. Once you’re fully comfortable with HTML, dive into CSS, working slowly. CSS has lots of different levels and options; take your time and make sure you’re proficient with the basics before heading into the more advanced aspects of it. Both are valuable to learn and with time, effort, and, of course, practice, you’ll be well on your way to reaching proficiency in both HTML and CSS. And after that, the world is yours! You can explore programming languages and pick your next victim. To get started on your web development journey, there’s no better way to learn HTML or CSS than with a bootcamp. At Ironhack, our Web Development Bootcamp is designed to provide you with the foundational knowledge necessary for entering the workforce, and HTML and CSS are integral parts of our coursework . You’ll leave comfortable with both--are you up for the challenge? We can’t wait to see you in class.