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April 26, 2022

How Much You Can Earn in London As a Data Analyst

Find out how much you can make as a Data Analyst in London, and how to break into the UK data industry.

Ellen Merryweather - Senior Content Manager

Data Analytics

London

Interested in what a Data Analyst salary in the London looks like in 2022?

We’re all here because we want to have a job that we love, or to help you get a job you love. But let’s not pretend there aren’t bills to be paid! Your savings account deserves as much attention as your job satisfaction. The data industry is huge, and it’s growing even still. In the UK, it offers superb opportunities for those with the skills and drive to succeed.

What are the current average salaries for a data analyst?

The average salary for a data analyst in the UK is currently around £28,750 for an entry-level job, and salaries can rise very fast with progression and experience.

Data analysts with a degree earn from £29,000 in their first role on average and those at entry-level but without a degree usually start at around £24,000. Data analysts are in high demand as the field is growing rapidly and analysts working in a big firm are usually earning around £35,000 after just a few years.

Once you’ve got a few years of experience under your belt a good Data Analyst can earn on average around £60,000, which is comparable to consulting roles in the field.

If you're outside of London, even though remote work is evening out the playing field, the company's location will greatly influence the salary on offer. Data Analysts working in the South of England can expect to earn more than those elsewhere. Across the UK, larger cities will most likely be offering higher compensation, but the cost of living is also likely to be higher than rural areas. You'll have to do the maths to figure out whether the salary a company is offering can support your lifestyle...but luckily Data Analysts are pretty good with numbers! And in such a well-paying field, you shouldn't have to tighten your purse strings anyway.

What does a data analyst do?

Today's businesses are completely driven by data, which governs every decision across every function. From deciding which customers to target to identifying ways to reduce waste in the business, managers need to access timely, accurate and rich data upon which they can steer the business.

Everyone in a business needs data, but not everyone is completely data literate. Marketers need to understand how touchpoints with their users are attributed, to figure out which channels and campaigns are working best. Product Designers need to know where the digital experience they’ve built is working and where people are dropping off, in order to make improvements. Leaders and c-suite need to understand performance metrics in order to make key business decisions. But these individuals may not be the best at looking at a spreadsheet full of raw data, and at extracting the insights they need.

That’s where Data Analysts come in.

Data analysts work with each business function to provide vital data. The job certainly involves looking at the numbers, but data analysts take things further and know how to use data that allows organisations to make informed choices. This might include producing reports, visualisations, summaries and other methods of data presentation that bring raw numbers to life and provide context and insight. These roles are already greatly in demand and the field is growing fast as businesses deal with increasing volumes of data and have to find ways to manage and organise it.

What sorts of things do Data Analysts do?

Data analytics combines practice and theory to pinpoint and share data-driven insights that allow organisational stakeholders to make strong decisions. Experienced analysts will consider their outputs in a broad context, including organisational factors such as the competitive environment, when they make updates and recommendations to their internal customers.

Data analysts will be skilled in fields such as probability theory, data visualisation, statistical modelling, risk management and predictive analytics. They will usually also be adept with database languages, programming languages nad other software languages. Data analysts will work with descriptive, diagnostic, predictive and prescriptive analytics which build upon each other to add increasing value to the business.

In a typical week, a Data Analyst might:

  • Design and manage a data system and series of databases, ensuring that they are free from errors

  • Mine data from a range of sources and then organise it for easy reading

  • Use statistical tools to analyse data sets and look for patterns and trends that are key for diagnostic and predictive analytics

  • Explain how the data results sit within the broader environmental context

  • Prepare reports that include visualisations of data, patterns of data, and predictions

  • Work with internal and external partners such as engineers, programmers and business leaders to identify ways that processes can be improved, systems developed and data policies for governance strengthened

  • Produce and manage data analysis documentation and processes

Data analysts tend to take on increasing leadership roles over time and become internal consultants with strong communication, management and functional skills. They work closely with senior business leaders to help them make sound decisions with the in-depth data and insight they provide into the business and its operation. This makes data analytics a good choice for a career changer who will have many of these existing higher-level skills and can build their technical base through further targeted study.

What's the best way to get a data analyst role?

Those lucky enough to realize what their dream job is while they’re still youthful teenagers may choose to pursue a university degree in Data Science, Data Analytics, or even Big Data. But it takes years of full-time study and thousands of pounds to earn a traditional degree, and without hands-on experience graduates still find themselves fighting over entry-level roles.

Others may look for an IT apprenticeship to fill their skills gaps, and gain entry to the data industry through on-the-job learning. These apprenticeships aren’t as regulated or well established as degrees, and can be hard to find. You really do have to be in the right place at the right time…or know just the right people.

At Ironhack, we designed our Data Analytics bootcamps to fill the gaps left by traditional education. You don’t need to quit your job and go back to university for 3+ years, as you can earn your Data Analytics certificate in as little as 9 weeks full-time or 24 weeks part-time. And with our dedicated careers services, we’ll help you meet the right people at the right time, (with the right resume to show off your new skills!)

Take the guesswork out of breaking into the UK data industry, and check out our Data Analytics bootcamps! 

Join our Hybrid Data Analytics bootcamps in London

If you’re weighing up the benefits of learning online vs on-campus, have you ever considered what a hybrid learning environment would be like?

With our Hybrid Data Analytics bootcamps you get the flexibility to choose between learning from our live remote instructors in the comfort of your own home, and spending time on-campus with your fellow students. Take your online classes from your sofa, from your favorite hipster coffee shop (no shame, we’ve all got one!), or from our awesome Shoreditch location.

It’s all the flexibility of remote, but with direct access to the London tech community and local Ironhackers!

Interested? Find all the info you need right here.

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How Learning Data Analytics Can Change Your Life

Ironhack - 2021-11-28

Data Analytics

Retraining for a career in web development or data analytics is more than just a change in the way you work. For many people, learning data analytics techniques can totally change the way they view the world, with consequences that go way beyond their professional tasks. If you choose a career change to data analysis, you can expect generous pay checks, plenty of choice about where you work, who you work for, and even how many hours you work. All of that is pretty much guaranteed. But in this article, we’ll be looking at benefits that are much more important. So let's dive in and explain what we mean. What Is Data Analytics and why is it so important? Before we go further, it helps to set out exactly what we're talking about. Data analysis involves taking huge datasets which often include millions of data points and turning this raw material into information that can shape the way we live. Doing this isn't simple. You need some knowledge of math and statistics, as well as coding skills and familiarity with analytics tools. All of that can be acquired via a good tech boot camp. And learning those skills can have huge implications for you and those around you. 1. Understand Public Policy at a Totally New Level One of the biggest immediate benefits of a career change to data analysis is that the world instantly becomes easier to understand. So many of the big questions that confront us are data driven. Think about pandemics and vaccine uptake, greenhouse gas emissions, inequality, and the way information spreads on social media. With an under-the-hood awareness of how data is created, you'll be able to pick out fake news and gain valuable insights into how the world works. That's a big deal for voters, investors, and ordinary citizens alike. 2. Become a Marketing Guru Overnight Another huge plus related to data analytics is the way it opens doors in the marketing world. So much of modern digital marketing revolves around data. Large and small companies need to know about click throughs, engagement rates, conversions, pay-per-click prices, keyword reach, and myriad other data points. Most managers aren't savvy when it comes to data, so they have come to rely on professionals to lead the way. So with a bit of data analysis experience, you could easily find yourself steering marketing campaigns and saving the fortunes of local businesses. 3. Gain a Totally New Appreciation of Pro Sports As any sports fan will confirm, data has always been a major part of the fan experience. Whether you're talking about batting averages, rushing yards, or 3-point accuracy, data and sports have a long relationship. But, the development of analytical technology in recent years has taken that relationship to another level. With strong data analysis skills, you can find ways to outfox friends in debates about Super Bowl contenders, or even find your way onto the ever-growing roster of data professionals at NFL teams. You can dominate Fantasy Football leagues as well, using your data awareness to leave competitors in the dust. 4. Become a Better Local Citizen Wherever you live, your local government is probably using data to inform its decisions about crime, zoning, environmental protection, housing, and - most commonly of all - traffic management. But, it's also likely that they are using this data inefficiently, failing to take advantage of the potential that data analytics holds. This means that when you make a career change to data analysis, you can also change the way you relate to local democracy. You might volunteer to help with civic campaigns, or even find work at City Hall. And if you want to go further, you could even put your data skills to work on election campaigns. These days, every election is data-driven, from the county level to the White House. 5. Make Your Workouts Go Further There's nothing geeky about data analytics. In fact, data analysis is also making a huge contribution to the way we exercise and stay healthy, and the more data skills you learn, the more effective your workouts can be. You can map your running distances, calories burned, heart rates, and routes to find a varied and effective cardio routine. Or, you could use similar data to work out how to peak for competitions, bringing in nutrition and sleep to complete the picture. And with the right tools, it's even possible to minimize the risk of injury and fatigue, helping you stay healthy for longer. 6. Pass on Key Skills to Your Kids Data analysis is also the kind of skill that children need to know about. They are going to live their lives in a world dominated by data, so understanding how it is collected, analyzed, and used is an incredibly valuable skill. Can you be confident that kids will learn data awareness and the skills needed to launch careers in this data-driven world? Perhaps. But if you have those skills and pass them on, your children will be in the pole position from the start. 7. Change Much More Than Your Career by Learning Data Analysis Everyone knows that this is the age of Big Data, but fewer people appreciate how data can change the way they live on a personal, day to day basis. As we've seen, making a career change to data analytics isn't just about job security or corporate progression. It opens a huge range of doors in everyday life from watching or playing sports to being a democratic citizen or helping out struggling businesses. The good news is that analytics aren't hard to learn, even for novices. Just enroll at Ironhack for an intensive Data Analytics Bootcamp , and you'll soon see the world in a totally new way.

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7 minutes

Why Data Analytics is the Future of Business

Ironhack - 2023-05-30

Data Analytics

Companies have never had so much power in their hands: for the first time ever, they have incomparable amounts of data to use as they see fit to make informed decisions. But the amount of data they have is only increasing; companies will have to continue harnessing that data to ensure that they are making the best possible decisions for their team. And how can they do that? What’s the best way to use data to make good decisions? How can data be processed safely? We’ll cover these questions and much more in this post. Data Analytics: What is it? Simply put, data analytics collects raw data and turns it into valuable information . This means that those who have the data make sense of it and analyze it to make the best possible decisions for the business. There are four types of data analytics : predictive data analytics, prescriptive data analytics, diagnostic data analytics, and descriptive data analytics. Let’s see how they differ: Predictive data analytics : this is the most common type of data analytics, used to identify trends, correlations, and causation and is made up of two specific sections: predictive modeling and statistical modeling. Predictive modeling : a campaign could look at average demographics to anticipate campaign results. Statistical modeling : statistical modeling would then be used to make sense of the data and draw conclusions. Prescriptive data analytics : here, big data and artificial intelligence come together to predict outcomes and identify future actions, using both optimization and random testing. Optimization : using optimization, data analysts can find specific areas that need improvement and work on that, keeping quality actions in place and just correcting areas that can be improved upon. Random testing : this can be used to create new, previously untried combinations to see if something unexpected happens. Diagnostic data analytics : the third type of data analytics is definitely the least exciting, but is extremely important for improving; diagnostic data analytics looks at past results to make decisions using discover and alerts and query and drill downs. Discover and alerts : these preset alerts help you anticipate problems before they occur, using past data to flag potentially disruptive situations. Query and drill downs : these further break down data, explaining why something occurred using data. Descriptive data analytics : the other forms of data analytics would be useless without descriptive data analytics; having important data is great, but only if it can be properly presented using ad hoc reporting or canned reports. Ad hoc reporting: these reports are created on the fly to address a specific issue and help provide in-depth data to fix a problem. Canned reports: this pre-designed report is regularly issued, such as a monthly or weekly report, to give an overview of a specific topic. Benefits of data analytics Despite the most commonly understood benefit of data analytics being using high-quality data to make the best decision, data analytics actually offers many advantages that are of great value to your company. Let’s discuss. Personalize the customer experience By collecting data from lots of different channels, you’ll be able to gather more and more information on your clients, helping personalize the customer experience even further. More information and data on customer behavior can make all the difference when it comes to making profits. Assist in large-scale business decision-making If the company has been witnessing financial losses or decreased productivity, data analytics can be used to analyze both past and future data to draw conclusions. Are certain employees performing better than others? Is the company’s remote work policy hurting overall productivity? Looking at company-wide data can help influence business decisions. Streamline operations Data can show where you need to improve or where efficiency and productivity are limited; seeing the data of how each part of the business is working can help you identify problem areas and use data to make high-quality decisions, especially in sectors where lots of change is experienced, such as retail (demand varies significantly season to season). Prevent risks and tackle issues Preventative data analytics comes into play here and is a great option for companies that are looking to avoid the potential risks of customer or employee theft, uncollected receivables, employee safety, and legal liability. Using predictive models, companies can predict future issues and address them before they happen. And when problems do arise, analysis of past decisions can suggest the correct way to handle them. Improve security As more and more companies use data to make decisions, the risks of cybersecurity are growing and becoming increasingly present. Using data of previous breaches or cyberattacks, corporate cybersecurity teams can see what went wrong, stop the attack, and take action to avoid a similar problem in the future. Data in business: how does it help? To make smart decisions, you must use data. And as the market moves at an incredibly fast pace, data can help you: Determine who your customers are Decide how to reach your customers Determine your specific market Define competitors Let you know what’s currently happening in the market Suggest future predictions Sounds like a lot, right? Well, it is. Data analytics can be the difference between making a smart business decision and a damaging one. After all, with proof of why something is working, you’re better equipped to make future decisions as well. Now that we’ve covered what data analytics is and its different kinds, let’s discuss how to run a quality data analysis in just five steps. How to Use Data to Make Decisions When a big decision is coming up, you might be torn on how to make it. Do you look at past data? Or future predictions? What about your gut feeling? It can be tough at the moment, but if you use data to make decisions, you’ll be better off and in just five steps, you can do just that. Step 1: Know what you want Okay, this seems like an obvious step, but it’s quite important: you can’t reach your ideal solution if you don't know what you want! Before you take any further action, make sure you create a clear vision of what you want to achieve with this decision together with your company’s future vision. If you’re unsure, take a look at your company’s yearly goals and key results to help guide you. Step 2: Find data sources Now that you have your vision clearly defined, it’s time to find places from which to extract data. After all, with just a vision and no data, you won’t be able to make a smart decision. To collect data, you have to decide the metrics you want to explore, such as the gross profit margin, return on investment, productivity, total number of customers, and recurring revenue. To then collect your data, reporting tools such as Microsoft’s Power BI let you collect data from multiple sources to have a global picture of your company’s status. Step 3: Organize the data Collecting data is great, but only if you know how to use it. In fact, the data you collect is actually useless unless you know how to put it into use. Once you’ve collected your data, make sure it’s clearly displayed and visualized so that data analysts can draw smart conclusions from the data. Executive dashboards present in the data in such a way that it’s clear and easy to read. Step 4: Analyze the data You’ve gathered your data and displayed it in a way that makes it clear to everyone: now what? Well, it’s time to actually analyze your data and draw conclusions that will guide your decision making process. Use both research that you’ve gathered yourself and from external sources to make the best possible decision. Step 5: Draw conclusions As you analyze your data, you’ll start to draw conclusions, but it’s important to make this an entirely separate step so that your conclusions stand on their own. While drawing conclusions, ask yourself the following questions: What conclusion am I drawing that I already had? What conclusions are new? How can I use this new information to meet business goals? The answers to these questions will help you successfully make your decisions and then bam! You’ve completed your data analysis. Your Future in Data Analytics Have we convinced you that data analytics is the career choice for you?! After all, the US Bureau of Labor Statistics predicts that the demand for data analytics roles will grow by 23% from 2021 to 2031 , much, much faster than the 5% increase in other industries. We thought so–at Ironhack, we offer a Data Analytics Bootcamp to prepare you to jump into the field, teaching you everything you need to know to be a competitive candidate for entry-level data analytics jobs in practically any sector . And once you’ve got a bit of experience under your belt, the world is your oyster: you can choose to specialize in a specific field or advance in your preferred role. With highly-competitive salaries and lots of room for growth, there’s no better choice. Check out our Data Analytics Bootcamp today and dive into tech–you’ll thank us later!

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7 minutes

The Role of Data Analytics in Cybersecurity

Ironhack - 2023-08-10

Data Analytics

Cybersecurity

Data analytics and cybersecurity are two separate fields of tech; however, if desired, data analytics can lead to stronger cyber securitization practices . Today, data analytics is essential for modern cybersecurity practices . Through utilizing data analytics in cybersecurity, organizations can expand beyond perimeter-based security measures and embrace a data-driven securitization approach. In short, tech professionals should not shy away from the power of data in enhancing their ability to identify and mitigate cyber threats. That said, let’s start by breaking down these two fields of work and learn what role data analytics should play in cybersecurity. What is Cybersecurity? As the digital terrain grows, the threat of hackers and data breaches is only expanding . Thus, cybersecurity is an ongoing process that looks to protect information technology systems from malicious actors. It’s an umbrella term to describe the multiplicity of processes that work to secure digital data, computer systems, and networks from damage, theft, unauthorized access, and other disruptions. It’s the responsibility of cybersecurity professionals to ensure the integrity and availability of digital data. Through active monitoring, assessment, and preventative work , cybersecurity professionals proactively establish strong defenses against online threats. What Responsibilities do Cybersecurity Professionals Have? Let’s break down some of the specifics of being a cybersecurity professional. Protecting and defending computer systems from the onslaught of digital threats is work that requires foresight, attention to detail, and an awareness of current cyber threats . Of course the work will vary based upon the organization, their work, and the type of computer networks used, but generally cybersecurity professionals’ responsibilities include: Risk assessment and management: identifying potential security vulnerabilities and assessing their risk is core to a cybersecurity professional’s work. They must then develop strategies to mitigate risks and allocate resources effectively in order to protect companies from potential attacks. Designing and implementing secure architectures: cybersecurity professionals must develop security policies, establish standards, and determine guidelines for the organization’s IT infrastructure. Detecting threats: threat detection and prevention is reliant upon the regular monitoring of network traffic and user activities in order to identify and block unauthorized use or malicious behavior. Equally, cybersecurity professionals must stay informed about emerging threats and evolving attack techniques. Response and recovery: refers to the process of developing and testing incident response plans in order to best confront and mitigate security incidents. After a security breach, cybersecurity professionals must determine the source, scope, and impact of the attack. This work requires strong coordination between internal teams and external stakeholders in order to contain and resolve incidents. Identifying vulnerabilities: much of a cybersecurity professional’s work is about predicting and preventing cyber threats. This happens through vulnerability assessments and penetration testing that help identify vulnerabilities in network systems. Once those weaknesses are identified, cybersecurity professionals must patch and update systems and applications. Teaching cybersecurity: an organization’s cybersecurity program is only as strong as the people who carry it out make it. Ultimately all employees play a role in securing network systems, and thus, they must be educated about cybersecurity best practices and safe online behaviors. Cybersecurity professionals often conduct security training workshops in order to promote a culture of cybersecurity throughout the organization. Compliance and regulations: when it comes to cybersecurity, organizations must be in compliance with government regulations and other industry standards. Cybersecurity professionals must conduct regular audits and assessments to verify compliance with data protection and privacy laws. Soft skills : cybersecurity professionals must possess strong interpersonal skills such as collaboration and communication in order to align security efforts. Collaborating with cross-functional teams and communicating security risks and mitigation strategies is crucial to an organization's security. Cybersecurity professionals must be active and vigilant in their work in order to safeguard organizations from a vast range of cyber threats . It’s a career that requires both soft and technical skills in order to fully maintain the confidentiality, integrity, and availability of an organization’s digital assets. What is Data Analytics? Data analytics is the process of breaking down, cleaning, processing, and transforming raw data with the intention of securing insights that help companies make stronger data-driven decisions. Data analytics is a huge field that uses a wide range of techniques and methodologies in order to uncover patterns, trends, relationships, and other valuable information from large datasets . Today, data analytics is utilized across industries to support organizations in making data informed decisions, solving complex problems, and optimizing their operations. The verstitle ways in which data analytics can aid companies is what makes it one of the fastest growing fields in the United States. The U.S. Bureau of Labor Statistics (BLS) estimates a 22% growth in the industry through 2030 , meaning that more than 3,000 new job positions in data science will need to be filled each year . Knowing that, what exactly does it entail to be a data analyst? What Responsibilities Do Data Analysts Have? Data analysts must collect, process, examine, and pull meaningful insights from raw datasets. The conclusions drawn from the trends, correlations, and relationships in data sets are then utilized to make informed recommendations to organizations on how to best optimize everything from production processes to marketing campaigns. The work varies depending on the organization, but key responsibilities include: Data collection: it’s upon data analysts to collect relevant data from numerous sources. These include databases, spreadsheets, websites, and social media platforms, among others. Data preparation: ensuring data quality and accuracy is part of the data preparation process. Raw data is often overwhelmed with inconsistencies, missing values, and other anomalies. Data cleaning is crucial in identifying and resolving issues pertaining to data accuracy and reliability. Data filtering : generally data must be transformed and organized such that it’s suitable for analysis. This involves updating, filtering, and normalizing data to make the analysis manageable and valuable. Exploratory Data Analysis (EDA): EDA is the process of visualizing and summarizing data sets in order to identify trends, patterns, and other correlations within the data set. Machine learning: this data analysis technique utilizes algorithms in order to build predictive models that give organizations insights into future outcomes based on historical data. Machine learning and predictive analysis are often used in fraud detection and customer behavior prediction. Visualization and reporting: this is a huge piece of data analysis. The insights uncovered are only as relevant as the analyst’s ability to communicate them to stakeholders. Data analysts use charts, graphs, and other images to communicate findings to non-technical stakeholders. These are just a few of the many responsibilities of a data analyst. It’s important to note that the work straddles technical analysis work and tactical communication skills in order to present findings. The field is constantly evolving with the introduction of artificial intelligence and other techniques that aid analysts in extracting insights from data sets. Cybersecurity vs. Data Analytics Data analysis and cybersecurity are two distinct areas of work, however, they overlap and can complement each other . As we know, data analysts focus on the collection and exploration of data, while cybersecurity professionals look to protect and make more resilient computer networks. That said, data sets need to be secured in order to ensure their integrity, while threat detection processes can be enhanced utilizing data analytics . In other words, if data analysts and cybersecurity professionals collaborate, they can strengthen the reliability of data analysis processes and improve network securitization systems . Let’s learn how! How Can Data Analysis Improve Cybersecurity and Data Privacy? Data analysis has shown to greatly improve cybersecurity through providing organizations with insights and tools needed to effectively detect, prevent, and address cyber threats and privacy breaches . If anything, this collaboration is only becoming more urgent as the industry standards surrounding cybersecurity and data privacy law transform. Thus, tech professionals should work together and build towards cybersecurity and data privacy. Cybersecurity data science Cybersecurity data science refers to the application of data science techniques to cybersecurity data . It utilizes statistical analysis, machine learning, and other data science methodologies to uncover patterns, anomalies, and relationships within cybersecurity data. From here, cybersecurity data analysts can create predictive models which aid in threat detection and can enhance the organization’s cybersecurity processes . Knowing the importance of data analysis in cybersecurity, organizations may decide to hire a cybersecurity data analyst . A cybersecurity data analyst is a tech professional who specializes in analyzing and extracting insights from cybersecurity data in order to identify potential security threats, vulnerabilities, and other relevant trends. Ultimately, their role is to carry out cybersecurity data analytics to gain deeper insights into the organization’s security landscape, and better protect sensitive data from growing cyber threats . Big data analytics in cybersecurity To take it even further, cybersecurity data analysts can integrate big data analytics into cybersecurity . The focus of big data analytics in cybersecurity is on utilizing advanced data analytics techniques to break down vast volumes of data , in other words, “big data,” that many traditional data processing tools wouldn’t be able to achieve. “Big data” includes the three Vs : Volume, velocity, and variety : there’s a significantly greater amount of data than standard datasets, it’s generated at a high speed, and takes diverse forms (video, texts, pdfs, graphics, etc). In the context of cybersecurity, big data analytics allows data scientists to extract insights from massive datasets including network traffic, logs, social media feeds, and threat intelligence feeds. The process can enhance traditional cybersecurity practices, leading to a more resilient security posture. These are a few of the many ways in which data analysis can improve an organization’s network security. If you’re interested in expanding your knowledge about these evolving industries, then you're in the right place! Ironhack offers bootcamps in both data analysis and cybersecurity that will give you the skill sets to jumpstart your tech career.

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8 minutes

Is Data Science Right for Me?

Ironhack - 2023-07-29

Data Analytics

The decision to make a career change (or even choose a career in the first place!) is a big one--how can you be sure you’re making the right decision? One of the most popular topics in tech right now is data and we know why: data drives practically every choice that we make . No matter the industry, data is necessary and to properly use data, companies need skilled data professionals and even within the field of data, there are tons of different roles. Data analysts differ from data scientists and data engineers have diverse responsibilities.   Data scientists are one of the most common roles in tech, but it isn’t for everyone. In this article, we’ll explain what data science is, what your role as a data scientist would be like, and how to decide if it’s the right choice for you. What is Data Science? This branch of tech combines math, statistics, specialized programming, analytics, artificial intelligence, and machine learning . Whew! That’s a lot–and that’s because data is becoming increasingly important and we’re relying more and more on data every day. Data scientists take on a more research-focused role when it comes to data, looking at large amounts of data and raising questions to improve business growth. There are five main stages of data science: Capturing : the first step is clear: you need data to work with! Collecting your data should be your first priority and once you’ve figured out what you want to achieve with your data analysis, you can start obtaining your data–both raw and unstructured. Maintaining : now that you have your data, it’s time to make sure that it’s actually useful, which you can do through data cleansing, staging, and organizing. Processing : your data is well-organized and sorted; so you must figure out exactly how you want to analyze it so that you can prepare it for the next stage. Analyzing : here’s the big part! It’s time to analyze the data, using predictive or exploratory methods to get the answers you’re looking for. Communicating : while analyzing your data might seem like the most important part, your findings are useless if you can’t properly communicate them to your team. Prioritizing data visualization can help the rest of your team understand the results. Okay, sounds clear, right? Let’s dive right into why data science is a great career choice for many and the benefits of the field. Benefits of using data science We hope that you’re already clear on why data science is so crucial, but just in case you’ve stumbled onto this article by chance, let’s recap: especially with large amounts of data, it’s easy to miss certain patterns or trends that could be key to the decision-making process. Data science can help you see and evaluate these patterns in your analysis , ensuring you’re making the most of your data. In addition, lots of industries are using data science to better the client experience: Healthcare : imagine if doctors could compare your symptoms and information to other patients, using past data from a wide range of worldwide records to better diagnose and treat you. Well, data science in healthcare is allowing doctors to do just that, improving overall healthcare. Streaming sites : Netflix does know what you’re looking for next and no, it’s not because they’re listening to you! Streaming services such as Netflix, Amazon Prime, and Hulu use the massive amounts of customer data to suggest new content to you once you finish a show; without data science tools, the sheer amount of data would be impossible to organize. Speech and image recognition : these two technologies use different techniques but process immense amounts of past data to recognize either speech patterns and words or images to properly identify them for the user. It’s clear that properly processing and analyzing data can have incredible effects on your company’s overall success, using your data to make better business decisions. And using data may even open up doors you thought were previously nailed shut: Reopening schools after the COVID-19 pandemic seemed like an impossible challenge. How could schools, especially those with lots of students, keep track of the sheer number of students, their contacts, and any possible symptoms? Or who had already contracted the virus? Through collecting data about symptoms and contact tracing, many towns were able to streamline the process of both reporting cases and informing students about possible contact with the virus, allowing schools to open up faster and safely. Autonomous vehicles seem like a thing of the future, right? After all, how could we reach a point where cars can anticipate human actions or even react properly in a split second? Well, data science is helping cars learn what happens on the roads around it, using data from surrounding cars to keep passengers safe. Now that we’ve convinced you of the importance of data scientists, let’s head a bit deeper into data scientists’ responsibilities and what you can anticipate as a future data scientist. Responsibilities of Data Scientists As a data scientist, you’ll be tasked with both collecting and analyzing large amounts of data to help your company make better decisions. Sounds a bit broad, right? Here’s an example situation to help you picture being a data scientist yourself: Your business is doing well, but you’ve realized that the majority of male-identifying visitors make one purchase and never return to your site. You’re happy that they’re encouraged to buy once, but want to understand why they’re not returning and how you can improve that in the future. First things first: you’ll work with colleagues to highlight problem areas. From these selected areas, you’ll create a series of questions to which you want answers; in this case: why men aren’t returning customers. With your goal in mind, you’ll focus on creating the proper data sets and variables so that you’re able to answer your questions. For example, if you’re worried about men not returning to make a second purchase, you’ll decide to collect data about your male customer base, the pages they’re looking at, their searches, and the products they’re buying. After you have this data, you’ll pick the right format for analysis, looking at the data to best determine the setup. You’ll also ensure the data is clean and complete, avoiding any mistakes that could compromise the integrity of your data. You’ll probably have lots of data, so you’ll use machine learning and big data tools such as analytic systems to run the analysis for you. Once you have your analysis, you’ll take a look and draw conclusions. You can see that many men have returned their product, citing sizing problems. Now that you have your answer, you can properly visualize the results for the rest of your team and communicate your concerns to the sizing department, helping ensure that the sizing becomes accurate and hopefully ensuring men will soon become return customers. Seems straightforward, right? Data science is incredibly valuable and more and more companies are prioritizing it, meaning there are lots of job opportunities out there for you. But before you head to a data analytics course, let’s clarify the main differences between the two. Data science v. data analytics The two are frequently conflated–and listen, we get it. They’re quite similar and do overlap; here’s a quick and simple guide of the areas in which they differ: Job description : data scientists typically need to have neural learning and machine learning experience and are responsible for the entire timeline when it comes to data analytics, from defining the problem to presenting the solution to stakeholders. Data analysts focus primarily on the actual analysis, although they might be tasked with more responsibilities in smaller companies. Required skills : both roles need to have some coding experience, but data scientists should also be familiar with big data frameworks while data analysts are typically fine with data analytic tool knowledge and a few programming languages. Career opportunities: both fields are growing at incredible rates and are predicted to reach new heights by 2030; no matter which you choose, you’ll be able to check out various job postings. How to Become a Data Scientist If you’ve made it this far it’s definitely because you’re totally set on becoming a data scientist–we’re so happy you’ve decided to make the jump into both tech and such a crucial sector. There are lots of ways to become a data scientist, of course, but here are some of our top recommendations: Brush up on your math and stats skills: data scientists need to be quite comfortable with data and mathematical functions; preparing yourself with some math review can help you land your dream job. Take a bootcamp : as we mentioned, there are lots of available jobs in data science and employers are looking for specific skill sets; check out what job postings are looking for and choose a bootcamp or online course that focuses exactly on that, preparing you to enter the job market immediately after graduation. Build up your secondary skills : data science encompasses lots of different areas, meaning a degree in just one area won’t prepare you for the workforce. Taking online courses or bootcamps to learn supporting skills, such as additional programming languages or to become familiar with machine learning or data visualization, can help set your candidacy apart from others. At Ironhack, we’re committed to providing you with exactly what you need to land a dream job after graduation with courses in web development, UX/UI design, data analytics, and cybersecurity, so no matter if you’re beginning your tech career or looking to add a new skill to your resume, our bootcamps are a perfect fit. We can’t wait to see you in class!

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9 minutes

Unveiling the Power of Data: A Journey into Data Analysis and Visualization

Ironhack - 2023-08-11

Data Analytics

Today, all companies are reliant upon data science in order to make stronger data-informed decisions, streamline processes, and secure the competitive edge in the market. Data analysis and data visualization are two interconnected components of data science and play an important role in extracting insights from data and communicating those insights to important stakeholders. On one hand, data analysis focuses on understanding data and extracting information from the patterns and trends within data sets. Data visualization , on the other hand, represents the findings of the data analysis process in a way that’s easy for non-data analysts to understand and interpret. The two processes are interconnected and have the same goal: extract valuable insights from data sets and communicate those findings in a way that can improve decision making and advance the organization. What is Data Analysis? We hear about data all the time, but how do companies actually use that information to inform their business decisions? That is the work of data scientists who play increasingly crucial roles in informing organizations of strategic actions they may choose to take. Data analysis is the process of cleaning, organizing, and interpreting data sets with the intention of extracting valuable insights from the patterns, relationships, and trends found in the data. Those insights inform company decisions , help develop organizational goals, inform marketing campaigns, and transform products in ways that are more aligned with consumer trends, customer profiles, or purchasing habits. What is Data Visualization? Data visualization is the process of transforming findings into graphical or other visual representations. The goal of data visualization is to represent complex data in a way that can be easily understood and interpreted by a broader audience . This audience may include other company teams, external stakeholders, or consumers: in other words, folks who may not come from strong data analyst backgrounds. Part of a data analyst’s role is to determine the best way to visualize data . This will depend on your audience, the type of data, and the story you’re helping to communicate through the data. These data visualization techniques may include: Graphs and charts : could be bar graphs, line graphs, scatter plots, pie charts, or other visual graphs. Maps : are used to showcase geographical data, spatial patterns, and distribution over space. Infographics : are a common way to combine numerical data with text in order to tell a story in a clear and concise way. Dashboards : are an interactive way to display data. Users are able to explore different aspects of data sets and gain a more holistic view of the data. In short, data visualization is a crucial component of data analysis. It’s a process in its own right that requires an understanding of your audience, the information they’re looking for, and how to best visualize your findings such that complex data is easily interpreted and key insights are highlighted. If you’re interested in a more in-depth take on data visualization, read about how to improve your data visualization skills on our blog. The importance of data visualization in data analysis Data is everywhere, but it’s meaningless if decision makers and other stakeholders can’t intuitively engage with it. This is why proper data visualization becomes so crucial. Data visualization is a major aspect of the data analysis process specifically because it brings data sets to life that may otherwise seem dull or overwhelming to many business professionals. Clearly communicating findings in a way that stakeholders can easily engage insights is what ultimately transforms data sets into a powerful resource for businesses . Here are a few simple ways in which proper data visualization may improve your organization’s decision making processes: Clarity and understanding : in the most basic sense, data visualization transforms messy data sets into easily digestible visuals that folks with non-technical backgrounds can easily engage and pull insights from. Providing a clear and comprehensive visual makes complex data sets less intimidating and offers an intuitive way for professionals to take part of the data analysis process. New insights : through presenting data in an alternative way, analysts and stakeholders alike may arrive at new insights or patterns that may have otherwise gone unnoticed. Through visualizing the data in an interactive way, viewers are empowered to take part of the analysis process, ask questions, and gain insights that may relate more directly to their areas of work. Enhanced decision-making: data is powerful because it leads to data-informed problem solving and decision-making. Well-designed data visuals can also communicate possible implications of alternative decisions and give greater insights to the potential outcomes of different choices and strategies. Streamlined communication: effective data visualization ensures that all teams and stakeholders have a shared understanding of the data analyst’s findings. This collective knowledge fosters collaboration and facilitates strong communication between teams and across disciplines. Comparisons : data visualizations provide graphics that can easily compare data sets, differing variables, or time periods. These graphics are particularly useful in looking at trends, comparing quarters, and noting transformations over time. These are just a few of the many ways that data visualization advances the data analysis process. As we know, data analysis is crucial in informing organizational strategy today, but only insofar as a data analyst can communicate their findings to other tech professionals and make the case for their data-informed recommendations. Through making that data digestible to a larger audience, data-based insights become more reliable , relatable to specific teams, and can be expanded upon to strengthen business decisions. Popular data visualization tools and techniques The world of data analysis continues to expand as more businesses invest in data sets and softwares to aid the data visualization process. That said, some of the most prominent data visualization tools include: Microsoft Excel for data analysis and visualization : Microsoft Excel is one of the most widely used spreadsheet tools that offers built in data visualization tools like charts and graphs. This program is recommended for folks who need quick data visuals for simple data sets. That said, Excel does not offer many of the newer data visualization tools that other specialized software may include. Tableau : Tableau is a powerful business intelligence tool that allows users to create interactive and shareable dashboards and data visualizations. The software is suitable for complex data sets, supports a wide range of data sources, and offers numerous chart and graph types. Power BI : similar to Tableau, Power BI is a strong business intelligence tool that supports users in creating interactive dashboards and data reports. It’s Microsoft’s business analytics software and thus works well alongside other Microsoft products. D3.js : is a powerful open-source JavaScript library that enables users to create interactive, dynamic, and highly customizable visualizations in a web browser. D3.js provides a high degree of flexibility for users over the design and character of the visualizations. QlikView and Qlik Sense : the Qlik tools allow data analysts to explore data, create interactive dashboards, and generate dynamic visualizations. QlikView is focused on offering pre-designed, complex dashboards and reports to end-users, while Qlik Sense’s interface is a self-service business intelligence platform that emphasizes user-friendly interfaces and collaborative development for both developers and non-technical users. It’s up to organizations to determine which softwares, tools, and techniques they wish to take advantage of in the data analysis and visualization processes. This will depend on the type of data organizations are delving into, as well as the audience that will later receive and engage with the data visualizations. Delving Deeper into Data Analysis and Data Visualization Data analysis is proving to play a major role in many of the fastest-developing industries from informational technology to financial services. In 2020, the World Economic Forum ranked Data Analysis as the second fastest growing job in the United States, with 25% growth before 2030. That said, if you’re looking to advance in the field of data science, earning a data analysis and visualization certificate, signing up for a data analysis and visualization bootcamp, or simply taking a data analysis and visualization course may be a valuable decision. If you’re committed to diving deeper into the world of data analysis and visualization, here are a few ways we’d recommend expanding your credentials: Earning a data analysis and visualization certificate There are numerous data analysis and visualization certificate programs to choose from. Choosing the right certificate program is crucial in measuring your knowledge and skills against the industry standards . Equally, it’s important to select a certificate program that teaches the skillsets you’re seeking in order to be successful in the specific areas that you’re looking to advance. Knowing this, here are a few of the most popular data analyst certifications in 2023: Google Data Analytics Professional Certificate : Google offers a flexible online certification that provides a solid foundation to the world of data analytics. The course is a “learn at your pace” program, with most participants completing the program in just six months. The certificate is designed for beginners with no prior technical experience with the intention of teaching the basics of data cleaning, visualization, and analysis alongside instruction of how to utilize spreadsheets, SQL, and R programming. Microsoft Certified: Power BI Data Analyst Associate : Microsoft’s Power BI certificate will teach participants the basics of Microsoft Power BI, instructing on how to identify and acquire data, transform data into data models and visuals, and later share those visuals and insights with relevant stakeholders. IBM Data Analyst Professional certificate : the IBM Data Analyst Professional certificate is a flexible online course that promises to prepare folks with little to no experience in data analysis for entry level data analyst positions. The course walks through Python, SQL, Excel, and IBM Cogno analytics. Signing up for a data analysis and visualization bootcamp: If you’re looking to jump deeper into the world of data analysis, we’d recommend checking out our Data Analytics Bootcamp : Bootcamps offer an immersive opportunity to dive deep into the subject matter in a relatively short amount of time. With Ironhack, you’re able to sign up for a full-time or part-time data analytics bootcamp that will have you learning an up-to-date curriculum alongside industry experts. Our Data Analysis Bootcamp begins with data prework and is followed by three modules that slowly increase in intensity. We begin with an introduction of data analysis and Python; this work folds into advanced data analyst work and finally, you’ll go through the fundamentals of machine learning–something that’s unique to Ironhack, earning our bootcamp the “ Intelligent Award: Best for Machine Training ”. Ironhack’s bootcamps are intentionally designed to get you career ready and we’re committed to supporting our students through our financing options , career services , and strong alumni network . If you’re interested in learning more, check out our Data Analytics Bootcamp and start transforming your career.

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6 minutes

How to Improve Your Python Skills

Juliette Erath - 2023-08-23

Web Development

Data Analytics

Learning a foreign language isn’t a one-and-done experience and that’s true for programming languages as well. Whether you’re new to Python or want to polish your skills, consistent practice can seriously impact your fluency. Unlike other languages, you can’t just hop on a plane and start chatting with native speakers, but there are a few things you can do to add regular practice to your routine and improve your Python skills. A Beginner’s Guide: What is Python? Just in case you’re brand new to programming…what even is Python anyway? Commonly touted as the more beginner-friendly programming language, Python can be used in web development, task management and automation, design, AI and machine learning, and data analysis. It’s an open-source language with a thriving online community and countless applications. It’s a perfect choice for those just starting out in their web development careers, as it’s such a flexible tool to have in your kit, it’s easy to learn, and there are plenty of beginner-friendly projects to help you improve. If you don’t already have Python on your computer, download it from Python.org and decide which code editor or integrated development environment (IDE) to use. Some IDEs that work in Python include Pydev, Pycharm, Sublime Text, and Visual Studio. How to Practice Python Online It’s possible to find online platforms where you can practice Python without installing it on your computer. You can use browser labs, some of which are mobile-friendly. Coding on smartphones isn’t easy, and you shouldn’t rely on it. However, depending on your circumstances, you can use it for practice. If you’re using an Android smartphone, you can install applications like Pydroid, Dcoder, and Acode. Once you install them, you can practice coding offline. For iPhone, you can install applications like Pythonista and Pyto. You don’t need to carry your laptop everywhere you go to practice Python. With an online Python editor , you can start practicing from any computer. You don’t need the administrator’s permission to install the program since everything is accessible online. Best of all, this type of tool is free, allowing you to build, test, and share your code online–and you don’t have to worry about updates. Join an Online Python Community While your community of fellow Ironhackers will undoubtedly have some Python know-how (especially our Web Development and Data Analytics students), you may want to broaden your horizons and meet others who are learning or upgrading their Python skills. Try checking out the official Python community where you can meet other enthusiasts, learn more from seasoned professionals, and maybe even do some networking. Check Out Some Popular Python Books If you love a good book, there are plenty of great reads out there about Python and programming. We’ve handpicked these recommendations for you: Python From The Very Beginning by J. Whitington: this offers you a more hands-on experience, presenting you with questions to work through on your own time (don’t worry, all of the answers are inside!) Leaving the Rat Race With Python: An Insider’s Guide to Freelance Developing by Christian Mayar and Lukas Rieger: one for all of you entrepreneurs-at-heart who dream of building your own coding empire. Python 3 Object-Oriented Programming by Dusty Phillips: once you’ve mastered the basics of Python and you’re ready for the next step, give this a try! It’s the perfect tool to take you from beginner to a more confident programmer. Get Involved in Online Python Projects Building on a project is the best way to learn Python; it provides an opportunity to implement everything you learn. And it’s what employers look for when hiring programmers. The kind of project you choose will depend on your fluency in Python, your areas of expertise, and how much free time you have available. You may also choose to tailor your project to the industry, niche, or company of your dreams, as projects are great job-hunting fodder. But what if you have no clue where to start? Let’s see: You can build what you see others building or find Python exercises online . Try to pick something fun to get you interested and keep you motivated to finish. Regardless of what you choose, it will still help polish your skills. Contributing to GitHub online projects is a great way of improving your coding skills. GitHub has thousands of open-source projects that run simultaneously. It’s more likely that you’ll find a Python project that suits you and best of all, the platform has a place for all skill levels. If you’re new, you can find beginner-friendly projects. Plus, you can work on project documentation since it doesn’t involve any coding. Such projects involve finding errors in the code made by others and if you have advanced skills, GitHub has endless opportunities for you. Working on open-source projects also helps you learn from others and build relationships with online communities. You’ll improve your resume by showing that you can collaborate with other developers, and it’s a good way to build up your confidence. Tips For Practicing Python Consistency is crucial when it comes to practicing programming. So make it a habit to code every day. But keep in mind that breaks are just as important and maintain a schedule that alternates coding time with break time. If you join a class or an online forum, don’t just focus on learning from others. You learn better when you teach. If you can explain a concept correctly, it shows you understand it. And if you don’t, you can identify areas where you need improvement. Programming can be challenging if you skip the essential rules taught in the introduction. So don’t ignore Python theory and programming blogs, but keep in mind that practicing requires you to build code in-person. Knowledge without implementation doesn’t make you a good programmer. Finally, don’t hesitate to ask for help when you encounter a problem. Doing so will save your time and limit the risk of feeling frustrated when something doesn’t work out. If possible, practice coding as a group of two or three people. That way, you can learn faster and generate more ideas. Learn Python From the Experts These are all great ways to keep practicing Python in your own time. But if you’re just starting out or you’re thirsting for as much tech knowledge as possible, learning from home may not be the way to go. It can be challenging to stay consistent and to make sure you’re learning the right things. An Ironhack Web Development Bootcamp lets you join a global network of alumni, as well as getting you into your local tech community. And you know you’re learning everything you need to know for a blossoming tech career with us!

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5 minutes

Exploring Machine Learning Algorithms: From Regression to Clustering

Juliette Erath - 2023-08-25

Data Analytics

You know machine learning involves computers solving problems that would take humans a lot of time and effort, right? You’re halfway there then: learning about machine learning algorithms are the next step to understanding how to use machine learning to your advantage. In this article, we’ll cover the general importance of machine learning algorithms and the most popular ones that can help you make the most of your machine learning tools. What are Machine Learning Algorithms? Machine learning algorithms are simple: they’re mapping methods used to learn about and identify underlying patterns in sets of data . As Arthur Samuel put it, machine learning is “a computer’s ability to learn without being explicitly programmed” and as computers continue to process and sort more and more data sets, they learn more and more about the best ways to handle said data. Why are machine learning algorithms important? We know that machine learning algorithms use past data to predict future outcomes, which can lead to: Higher optimization rates Effective fraud detection Better decision making Improved disease/treatment prediction Proper sales predictions There are four types of machine learning methods, within which the main kinds of models fall: supervised, semi-supervised, unsupervised, and reinforcement learning. Supervised learning Supervised learning sounds just like it is: machines that are fed a known data set by an expert, with included desired inputs and outputs. From there, the machine has to decide how to arrive at those inputs and outputs through identifying patterns in the data, making observations to arrive at predictions. In this case, the expert corrects the machine if needed and continues to work with it until it can guarantee high rates of accuracy. Experts using the supervised learning method can choose from three different algorithms: Classification : when using the classification algorithm, the program uses observed values to sort data into given categories. To do so, it uses observational data and improves the accuracy of its sorting as it gets more and more practice. Regression : when using the regression algorithm, the program is asked to estimate and understand variable relationships, using one dependent variable and other changing variables to predict what will happen. Forecasting : when using the forecasting algorithm, the program evaluates past and present data to predict future outcomes; it also analyzes trends to forecast the future. Semi-supervised learning There’s one key difference between supervised and semi-supervised learning: in semi-supervised learning, programs are fed both labeled and unlabeled data so that the program must take the labeled data and draw conclusions from the unlabeled data, drawing conclusions on patterns, trends, and future predictions. Unsupervised learning Just as the title suggests, unsupervised learning means the machine learning program works on its own, without labeled data or an expert , to find patterns and trends in the data. It’s given large data sets and organizes the information as it sees fit; as it receives more and more data, it’s able to become better at sorting the data effectively and properly. Two examples of unsupervised learning methods are clustering and dimensionality reduction: Clustering : when using clustering, unsupervised learning programs will sort the data into categories based on criteria they decide; they’ll then look for patterns and trends within those clusters to draw conclusions. Dimensionality reduction : to make it easier for unsupervised machine learning models to find patterns and trends, dimensionality reduction asks it to only look at a limited number of variables, reducing the overall requirements of the program. Reinforcement learning Our last category of machine learning algorithms is a bit different from the others; to help the program learn and advance over time, the algorithm is provided with a set of actions, paraments, and end values and from there, it’s asked to try out each and see which performs best. This helps the program learn best practices and try out different methods. These four ways to teach machine learning programs how to handle and analyze data are the main ways to categorize how programs learn, but we can choose from lots of different algorithms when it comes to actually analyzing our data. Let’s cover some of the most popular machine learning algorithms so that you can pick the best one for your next project. Machine Learning Algorithms Linear regression One of the most common and preferred algorithms, linear regression identifies a relationship between independent and dependent variables on a line, through the equation Y = a *X + b. Here, Y represents the dependent variable, a is the slope, X is the independent variable, and b is the intercept. Through this equation, the program is able to order and sort the data. Logistic regression Another common algorithm, logistic regression is used to separate specific variables from a larger set of independent variables and is valuable when it comes to predicting the probability of something happening. To improve the overall logistic regression model, interaction terms and non-linear models are frequently employed. Decision tree The decision tree algorithm is a supervised learning algorithm that classifies data successfully, using the data’s strongest qualities to divide it into multiple groups based on independent variables. This is one of the most commonly used algorithms for data categorization. Support vector machine algorithm To help visualize the data, the support vector machine algorithm allows you to plot your data points on a graph (the size depends on the number of data points) and then see how things are spread out, providing an easy way to see patterns and understand the data as a whole. Naive Bayes algorithm The Naive Bayes algorithm is popular because of its ability to evaluate very large data sets and find what makes specific variables stand out from others; it’s easy to use and can help successfully classify variables and predict outcomes. There are so many different machine learning algorithms that we could tell you about, but we don’t have the time! But knowing which one is right for you means you need to fully understand the type of data you’re working with and your desired outcome. If you’re interested in becoming more of a data expert than you already are and using data to make quality decisions, Ironhack’s Data Analytics Bootcamp is the right place for you. What are you waiting for? We’ll see you in class!

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7 minutes

Unleashing the Potential of Artificial Intelligence: From Machine Learning to Deep Learning

Ironhack - 2023-08-27

Data Analytics

Artificial intelligence (AI), machine learning, and deep learning are becoming increasingly common terms in our world. They’ve moved out of the realm of tech specialists and into our daily lives through tools like text assistance, face recognition technology, and chatbots like ChatGPT. These technical advances have the potential to revolutionize industries, take on complex problems, and improve efficiency in all areas of our lives. We’ve only just seen the basics of what AI makes possible. What would it mean to unleash the power of AI and fully embrace the potential of machine learning and deep learning? Understanding Artificial Intelligence First off, let’s get clear on what we mean when we say, “artificial intelligence.” Artificial intelligence, or AI, is the process of simulating human intelligence in computers or other machines with the explicit goal of computers performing human-like tasks autonomously . These tasks include everything from learning, reasoning, problem-solving, and language understanding–functions that historically require human intelligence. Artificial intelligence systems are designed and trained alongside vast amounts of data in order to learn from patterns and make recommendations or decisions based on that information. Under the umbrella of AI, there are many types and techniques that let computers effectively perform tasks previously only carried out by human beings. Machine learning and deep learning are two of the most commonly utilized AI techniques and are crucial in harnessing the power of AI. What is Machine Learning? Is machine learning artificial intelligence? The short answer is yes. Machine learning, or ML, is a subset of AI that utilizes data and algorithms to imitate human activity and make predictions and recommendations based on that mimicked human behavior. Types of machine learning: There are four main techniques that machine learning utilizes in order to train algorithms and best simulate human behavior. These techniques include: Supervised learning: Supervised learning trains an algorithm based on a labeled dataset, where each data point is tied to a correct label or outcome. Through having a correct answer, the algorithm learns to map out input features that correspond with output labels. Once the algorithm understands how to sort the controlled data, it’s able to make predictions on new, unseen data. Examples of AI and supervised learning include, fraud detection, spam detection, speech recognition, and medical diagnosis. 2. Unsupervised learning: Unsupervised learning trains an algorithm based on unlabeled datasets, meaning that the algorithm’s goal is to identify patterns, structures, or relationships within the data. Unsupervised learning typically results in clustering, where similar data points are grouped together, or dimensionality reduction, where data is simplified and organized based on its essential characteristics. Examples of unsupervised learning include anomaly detection, recommendation services, and predictive modeling. 3. Semi-supervised learning: Semi-supervised learning is essentially a combination of supervised learning and unsupervised learning. Semi-supervised learning uses both labeled and unlabeled data in order to improve the performance and predictive ability of the model. Examples of semi-supervised learning include text classification, medical image analysis, and situations in which obtaining a fully labeled data set is too time-consuming or expensive. 4. Reinforcement learning: Reinforcement learning teaches an algorithm through controlling the environment by which the model interacts. The algorithm is taught through a carrot and stick approach, rewarding desired behaviors and penalizing incorrect actions in order to train optimal strategies and decisions. Reinforcement learning is commonly utilized in robotics, training autonomous systems, and in developing personalized treatment plans. These four techniques provide a base from which models are built in order to make strong predictions and decisions based on data. Determining which technique to utilize depends on the problem, the ability to access data, and the desired outcome. Applications of Machine learning: Machine learning is becoming widely utilized across industries in order to maximize efficiency and personalize experiences for consumers . It’s used by companies like Facebook, Google and Uber in order to improve their products and services. Other industries and applications include: Healthcare : ML aids in diagnosing diseases, developing new cures and medications, and predicting patient outcomes. Finance : ML is utilized in fraud detection, risk assessment, and plays an increasingly relevant role in algorithmic trading. Consumer experience : more and more companies turn to ML in order to better understand consumers and increase customer engagement through personalized shopping experiences and product recommendations. Manufacturing : ML helps optimize production processes through predictive maintenance, anomaly detection, and quality control practices. Marketing : ML optimize marketing decisions through predictive analysis, customer segmentation and targeting, and the personalization of product recommendations and marketing campaigns. In short, machine learning’s ability to process large volumes of data and transform those data sets into accurate algorithms to predict outcomes makes it an invaluable tool for optimizing organizational efforts. By leveraging ML’s capabilities, companies across industries can make stronger decisions and drive better business outcomes. What is Deep Learning? Is deep learning artificial intelligence? Again, the short answer is yes. Deep learning is a subfield of machine learning based on artificial neural networks in which multiple layers of processing are utilized to progressively extract higher level features from data . Deep learning models can recognize complex patterns and excel in tasks such as image and speech recognition, language processing, and generative tasks. Types of deep learning architectures Deep learning models are dependent layered architectures through which data is transformed . These models are composed of an input layer, one or more hidden layers, and an output layer. Each layer contains interconnected nodes (neurons) that process and transform data. Learning happens through adjusting the weights and biases of these nodes in order to minimize the difference between the model’s predictions and the actual outcomes. The “deep learning architecture” thus is the model’s neural network, designed to address specific tasks and data types. Deep learning architectures are often classified into supervised learning and unsupervised learning and from there, are broken down into numerous other structures. Some of the most notable architecture include: Convolutional neural networks (CNN): are designed for visual data processing. CNNs use convolutional layers in order to learn hierarchical features from images. They’re important for image classification, image segmentation, or object detection. Recurrent neural networks (RNNs): is a foundational deep learning network architecture. Its focus is on sequential data given that it maintains a memory of past inputs. RNNs are often used for time-series analysis, natural language processing, and speech recognition. Long short-term memory (LSTM): networks are a type of RNN that addresses the vanishing gradient problem and are well equipped to capture long-range dependencies in sequences. Gated recurrent unit (GRU): is also a type of RNN designed to capture sequential patterns while having a simpler structure. These are just a few of many deep learning architectures available. Of course, like all AI, deep learning is quickly evolving as researchers develop new architectures to improve performance across various domains. Applications of deep learning: Deep learning has been embraced across industries and has transformed our everyday lives through products like self-driving cars and virtual assistants like Alexa and Siri. A few of the most common applications include: Autonomous systems: deep learning is what makes possible many autonomous systems like self-driving cars, drones, and robotics. Its technology enables these systems to perceive and navigate their environments. Entertainment : deep learning continues to advance in creative industries, generating art, music, and visual effects. Image and video analysis: is one area where deep learning has proved to dominate. From image classification and video analysis to facial recognition, deep learning methods are applied. Natural language processing (NPL) : is quickly advancing through deep learning models that support language translation, language generation, sentiment analysis, and chatbots. Deep learning’s ability to learn intricate patterns from data is expanding what’s possible for new technologies and scientific advancements. Artificial Intelligence vs. Machine Learning vs. Deep Learning Artificial intelligence, machine learning, and deep learning are related concepts, but with specific definitions and scopes: Artificial intelligence is the broader concept of creating machines or systems that can carry out tasks previously designated solely to humans. Machine learning is a subset of AI that focuses specifically on the development of algorithms and models that allow computers to learn from data sets and improve their performance over time. Deep learning is a subset of machine learning that creates multilayered models in order to process, transform, and learn from large amounts of data. Unleashing the Potential of Artificial Intelligence Machine learning and deep learning are undeniably having a transformative impact on the field of artificial intelligence. These advanced techniques have revolutionized the capabilities of AI systems ; machine learning and deep learning have allowed organizations to confront complex problems previously considered beyond the scope of humans or machines. We’re well on our way to a future where intelligent machines play a significant role across industries and in all aspects of human life. Whether it's machine learning or deep learning, it’s undeniable that artificial intelligence is the future for organizations across industries . Gaining a basic understanding of the concepts is useful to all tech professionals and diving deeper into these concepts will surely prove useful as companies invest more and more into these technologies. If you’re interested in the topic of looking to launch your tech career, you’re in the right place. Ironhack offers bootcamps relating to data science that will prepare you to confidently step into the field. Not only will you study alongside top tech professionals, but you’ll gain the fundamentals of machine learning–something that’s unique to Ironhack, earning our bootcamp the “ Intelligent Award: Best for Machine Training .” Let’s dive in!

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5 minutes

AI Ethics and Responsible Innovation: Navigating the Moral Landscape

Juliette Erath - 2023-09-05

Data Analytics

You’ve heard about students using ChatGPT to cheat on exams and yeah, it’s not the ideal use of this super cool tool. But the moral dilemmas that arise with the use of artificial intelligence tools are becoming more and more widespread –and can have drastic effects. As artificial intelligence tools become increasingly common and developed, the moral implications of artificial intelligence are also gaining importance. Ethical concerns when it comes to artificial intelligence are completely valid; lots of aspects of human life are subjective and it can be nearly impossible to create a machine that can take all parts of human life into account. Here are some of the main ethical concerns when it comes to AI: Bias : an AI tool that’s trained with bias can have disastrous effects, eliminating entire groups from a hiring process. Privacy : can we trust machines with private and confidential information? AI tools need to be secure and foster a sense of security. Values : one person’s moral leanings differ greatly from the next; how can we train AI tools to take values into account if they are so different? Humans : part of human life is making mistakes–is making a superior and flawless decision-making process better? Or does it place machines above humans? However, we all know how incredible artificial intelligence is and how it can help transform our world for the better. Here are some great examples of how artificial intelligence is used positively: Limited human involvement : computer or robotic precision is simply superior to that of humans and whether we’re using robots to dive deep into the sea or process large amounts of data, humans are better off without these responsibilities. Increased productivity : computers don’t need to sleep, take a shower, or even eat–human productivity, on the other hand, is limited and can depend heavily on outside factors such as background noise, how they’re feeling, and stress levels. Allowing computers to run 24/7 to collect and analyze client data, for example, allows a company to collect much more information than previously possible. Better collaboration : doctors can benefit from better and more centralized patient information centers where they can see diagnosis information from other providers and see similar cases to give the best possible treatment options to their patients. Less bias : even when they do their best to avoid it, humans bring their own biases and experiences to the table; machines don’t have biases (and artificial intelligence tools are advancing to ensure they don’t include the biases of their programmers) and can help make some processes, like job selection, fairer and more inclusive. We could go on and on because artificial intelligence is truly transforming our lives and the way in which we’re using it daily will continue to disrupt our understanding of technology. But as with anything, there are various concerns to keep in mind when it comes to artificial intelligence: A lack of ethics : morality and being guided by ethics is an inherent part of humanity and our decision-making process; as of today, attempts to truly mimic a human’s decision making process, down to their emotions and morals, have not been successful. High costs : it seems like everyone and their mother is using AI tools, right? Even though the fun and trendy tools like ChatGPT are widespread and available to all, the more intense and developed artificial intelligence tools are extremely expensive to build and maintain, meaning it’s out of reach for many companies. A reliance on machines : it’s easy to rely on a machine that can do your job faster and more efficiently, but what if the server goes down? Or you can’t create the exact prompt you need to get your job down? As humans learn to rely more and more on machines, they can forget their own responsibilities and capabilities. The Ethics of Artificial Intelligence To ensure that the pros of artificial intelligence tools are not overshadowed by the ethical concerns of artificial intelligence, such as privacy worries or making decisions that should be made by humans, a certain set of guidelines have been created to help ensure artificial intelligence development stays on the right track. All artificial intelligence tools should be: Transparent : the way that machines arrive at conclusions should be understandable and make sense to humans, in addition to being as close as possible to the human decision-making process. Fair : any potential biases must be eliminated during the development process, making sure that all users of the machine will be treated equally and fairly. Accountable : the person or people responsible for the creation and management of the artificial intelligence tool must be clear so that there are steps in place in case something goes wrong. Private : all machines must respect and take into account both local and international privacy regulations, ensuring that user data remains both private and secure. Beneficial : the goal of all artificial intelligence tools should be to better and improve human life, ensuring that humans benefit from the tool’s existence and it doesn’t cause more harm than good. Robust : in line with the previous point, tools must be well-designed and free of errors or mistakes to ensure it’s effective and can do what it’s intended to do. To ensure that future artificial intelligence tools follow these guidelines and work to contribute to an overall better and more ethical tech sector, it’s going to take a new generation of AI professionals who are focused and dedicated to the cause. After all, change begins with us. At Ironhack, we’re up for the challenge: are you? We’d love to see you in class and begin to take on the ethical challenges of artificial intelligence with you. Ready? We’ll see you in the classroom!

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8 minutes

Beyond Siri: The Evolution of Natural Language Processing in AI

Ironhack - 2023-09-24

Data Analytics

When you think of artificial intelligence, you probably think of talking houses and robots that can do absolutely everything for us. And while movies and popular culture have led us to believe that robots will one day be capable of doing everything we can (and probably better than we can, too), artificial intelligence is so much more than that; it can recommend the next show for us to watch on our favorite streaming service or even track common symptoms across cancer patients to suggest the right treatment. While artificial intelligence does have lots of interesting and useful applications, it’s hard to ignore the super cool artificial intelligence advancements like ChatGPT, Amazon’s Alexa, or Apple’s Siri. And in fact, natural language processing is one of the most transformative advancements in artificial intelligence that really makes us feel like there’s a robot with us, helping to make our lives easier. But what is natural language processing? Is it separate from AI? How do Siri and Alexa actually work? We’ll dive into these burning questions and many more in this article. What is Natural Language Processing? Let’s start at the very beginning; natural language processing is part of the branch of artificial intelligence that uses both machine and linguistic knowledge to teach computers how to understand both text and spoken word, just like humans can. While this might sound very straightforward, natural language processing is more challenging than what meets the eye. Why? Well, human language consists of: Extreme variation from speaker to speaker : think about everyone you know who has an accent, be it a native speaker from a different region, a fluent speaker who has a slight hint of an accent, or someone who is learning to speak your language. Then, take into consideration the amount of dialects that exist and you’ll realize the challenge that understanding spoken word means. Mistakes, intentional or not: people frequently make mistakes when writing, but especially when talking, incorporating slang or jargon that can completely change the meaning of a sentence provides quite the challenge for computers. Irony and sarcasm : irony and sarcasm are staples in most people’s vocabulary, typically signaled by a facial expression or change in tone of voice. But computers, which can only process the exact words it’s being fed, struggle to understand exactly what the speaker means. Multiple meanings : lots of words have multiple meanings and its desired meaning depends on many things: the surroundings of the speaker, their body language, facial expressions, or tone of voice. This ambiguity forces computers to decide what the speaker’s intention is, despite not having everything they need to know to make that decision. To help computers tackle these challenges and fully understand exactly what they’re being fed, natural language processing scientists work to help computers become even more capable of properly identifying and responding to human speech through the following methods: Speech recognition : for a program to respond or understand what a human is saying, the first step is to convert spoken words into text for the computer to digest. Seems simple enough, right? It isn’t, because this involves separating mumbled or mispronounced words, in addition to understanding various dialects or accents. Grammatical tagging : computers have a better chance at properly understanding human speech if it can identify the part of speech, such as a verb, noun, or adjective. Through teaching computers what words mean when used in specific grammatical structures, they better understand human speech. Named entity recognition : instructing computers to identify certain words such as countries or names of places helps avoid any confusion and recognize patterns when it comes to mentioning specific places/names. Sentiment analysis : this is one of the toughest to enact perfectly, as it requires a deep understanding of human irony and sarcasm usage; showing computers examples of when emotions affect the meaning of the sentence help the overall analysis of the speech be more accurate. Now that you’re clear on what NLP is and the challenges we face, let’s review the history of NLP and see how we’ve arrived at the NLP we know today. The History of Natural Language Processing Artificial intelligence began as futuristic ideas of robots and only became a reality in the 1950s as computers began to exhibit intelligent behavior that scientists like Alan Turner researched to harness the power of artificial intelligence . Although NLP study began in the 1950s, it really took off in the 1980s as machine learning algorithms were introduced to assist with language processing. Machine learning consists of providing machines with vast amounts of data that they can process and then make sense of. Through this, they identify trends and patterns that can help them make better decisions and analyses. Over the years, NLP has become even more advanced, thanks to more and more data. Natural Language Processing Today Today, one of the most common examples of natural language processing is Siri, Alexa, and other voice assistants. Let’s discover how NLP technology has created this seemingly personal assistant that’s ready to assist us with whatever we need–and can understand our speech. Major companies like Apple, Amazon, and Google have all created their own versions of virtual assistants, so we’ll be referring to them generally and discussing the technology behind them that makes them so useful–and innovative. When first released, people tended to have fun with the technology and ask for jokes or simply carry a conversation, but the truth is that these voice-guided virtual assistants are incredibly valuable. Voice assistants can: Follow your instructions to open an app, play a certain song, or make a purchase Give you directions to your destination Engage in a brief conversation Read from web pages/apps Tell jokes and imitate human reactions Give you reminders as scheduled These may seem like minor things that you could do on your own and yes, you probably could. But voice-guided virtual assistants bring the following advantages to your life: Increasing accessibility by not requiring any physical actions; in addition, for those who are visually impaired, read aloud options permit the user to fully use their device. Handling simple tasks like making a dinner reservation, sending a text, or setting an alarm. Automating repetitive and common tasks so that your time is free to handle more challenging or intense responsibilities. How voice assistants actually work Now that you know what they can do and why they’re so valuable, let’s break down the actual processing of voice assistants to better understand how natural language processing technology works here. Step 1: Voice Recognition This might seem like the simplest part of the entire process, but it’s actually the most complicated and requires advanced technology. Why? Because of everything we mentioned before; understanding the vast variations in human language is an incredible challenge and one that took a while to become a reality. To make sense of your words, the software collects your voice, converting it into a data file and sending it to servers. We didn’t mention this earlier, but another challenge is that the voice assistant must separate your voice from your atmosphere, eliminating any other speakers or ambient noise that complicates the process of understanding you. Step 2: Connecting to Servers Once your words have been converted into a file and sent to the server, it’s time for the magic to happen. Your words go through the different methods we mentioned above like grammatical tagging, speech recognition, and sentiment analysis to identify both what words you’re saying and what your true meaning is. Major companies like Apple and Google already have giant databases of resources to help with your query, but can also simply use the internet to find the answer to your question (which is why an internet connection is required for using virtual assistants!). Step 3: Understanding your Meaning With a clear understanding of what words you spoke, the software is now tasked with understanding why you said that, needing to figure out the meaning behind your words, taking into account your tone of voice, inflections, and any irony. And natural language processing takes front stage here, making sense of your meaning, not just the words. Drawbacks of natural language processing We’ve advanced significantly in recent years, but there’s still a lot of work to be done to improve the technology. As of today, these are the most common drawbacks with technologies like Alexa and Siri: Listening issues : in loud environments or when multiple people are talking around you, voice assistants may be unable to hear you or even determine who it should be listening to. Dialect/accent issues : users who have a strong accent, be it in their local dialect or second language, can struggle to be understood by voice assistants, especially if they aren’t fully familiar with how to accent the words properly. Language limitations : despite being known worldwide, most voice assistants are limited to English or a few other languages like Spanish, French, and German. For voice assistants to be truly universal and inclusive, all languages must be included. However, this will require a significant investment into the data required to make NLP work in these languages. Internet access : because the software pulls from the internet to evaluate speech, internet access is required to use voice assistants, which can limit both their accessibility and function. As you can tell, natural language processing has advanced significantly over recent years, culminating in the creation of something that can help make our lives easier every single day. And at Ironhack, we’ve recently revamped our curriculum to reflect these changes in the tech world, ensuring our graduates are prepared to enter the workforce with the knowledge they need to land their dream jobs. If you’re interested in taking that first step towards learning what you need to know about artificial intelligence, you’re in the right place. Check out our bootcamps today and become the next AI expert.

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Ironhack News

5 mins

5 Trends and Hiring Predictions for 2023

Gabriel Pizzolante - 2023-01-18

Careers

All Courses

London

A new year starts and we're excited to see what the tech sector is going to bring for us. While we can't know the future, we've consulted with some of the Ironhack team to give you our predictions and tech trends to watch in 2023. Here is what our team had to say: Don’t be put off by the tech layoffs story - digital skills are still in demand Recent news has been dominated by layoffs and hiring freezes in the tech sector. The reality is that the digital sector will still face a skills gap that is much bigger than the layoff crisis. From an Ironhack perspective, the number of students joining its courses and company hiring partners has not seen a decline. José Maria Garrett, Global Outcomes Lead at Ironhack, explains: "The demand for digital skills from companies in the tech sector has not declined in the last quarter of 2022. Even in a more risk-averse economic environment, tech employment continues to increase because of that demand for skills. In reality, the biggest change we're seeing is that mid and senior talent profiles are changing jobs less frequently vs. the abnormal behaviour we saw during and immediately after COVID. We have shifted from "the great resignation" into a new stage: "the great renegotiation." Garrett continues: “It's a great time for junior talent to leverage new skills development and access the tech sector within more traditional industries which have an increased demand for those digital skills.  It also provides an opportunity for scale-ups and SMEs to acquire great talent that might not otherwise have been available to them whilst employed in larger tech companies.” Generalists vs. specialists shifting in tech organisations Felipe Rocha, Lead Teacher in Data Analytics at Ironhack, explains a shift from specialists to generalists. According to Felipe, “The job market is changing and we can now see generalists thriving as data professionals. The wide range of knowledge that Ironhack bootcamps equips students with improves their ability to make connections that specialists might not. Having a professional that is capable of "wearing different hats" and navigating the vast world of data can be a big win, especially for smaller tech companies and teams”. Gabriel Pizzolante, UK Growth Marketer at Ironhack, adds: "At Ironhack our bootcamps prepare people for a wide range of skills within the field that they choose, making it easy to become a more generalist professional with the option to specialise if that’s what they require in future. For example, our Web Development bootcamp equips them with a full-stack development skill set that can then be developed towards front-end or back-end development in any industry of their choice. Our UX/UI Design bootcamp provides concepts and frameworks of user experience, user interface and basics of front-end development. These can then be used in a multitude of roles as the Ironhack graduate finds opportunities and gains more experience.” Bootcamp uptake will exponentially increase not just from career changers but from school leavers Skills bootcamps like Ironhack have always been a great alternative for career-changers to launch a whole new career in a very short timeframe. The intensity and the level of commitment required offers an effective alternative to having to go back to university to pursue a dream. Pizzolante explains: “While we are very focused on the career-changer, we are starting to see more applicants who are dropping out of university because it wasn’t right for them.   What we’re observing is that Gen Z is taking a new approach to higher education. They value professional development at a faster pace over and above a traditional career path.  In 2023, we expect to see an increase in school leavers taking bootcamps as a fast-track alternative in just nine weeks, as opposed to taking years to study for an undergraduate degree.” Employers will prioritise spending on upskilling career starters and reskilling existing teams over hiring senior tech professionals Over the past decade the more experienced tech professionals have been in high demand, making it very hard to retain them. With changing market conditions, we predict that more companies will shift the focus to invest in their existing teams to develop them in-house, foster loyalty and retain them for longer. By having the right infrastructure, more employees within companies will be able to shift careers to technical roles and there will be more opportunities for bootcamp graduates to gain the development they need within a tech company. When it comes to reskilling, according to Jan Molendijk, Lead Teacher in Data Analytics at Ironhack, “In today's super competitive job market, with quiet and not-so-quiet quitting, employers have no choice but to use the potential they already have in house. It is easier and cheaper to retrain employees that are competent professionals in newer technology, than it is to find new people that have had recent training in that new technology. The big plus is that these people already speak the language of the company. And this is also a win for the employees, they not only keep their job, but gain valuable new skills, knowledge and experience”. Energy-efficient technology design will become more popular The world is facing an energy crisis, affecting not only the UK but most countries around the world. Saving on energy is increasingly a priority.  For this reason, the use of technology should also be a consideration. According to Ironhack’s course leaders, developers are being wiser about creating and optimising code that does not consume high levels of energy in every-day or weak devices. Mathieu Subin, Lead Teacher in Web Development at Ironhack, comments, “The way code is being optimised for use is more similar to old technologies like we would see on cartridge games decades ago. This may lead to new design patterns and/or paradigms”.

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9 mins

A Chat with ChatGPT on Tech Careers and Bootcamps

Gabriel Pizzolante - 2023-02-16

Careers

London

All Courses

It feels like everyone is talking about ChatGPT . If you’ve tried to check it out and found yourself locked out because too many people are using it…you’re not the only one! When we finally managed to find time in ChatGPT’s very busy social life, we wanted to ask it a few burning questions. How exactly does this technology work? What does it know about the current and future state of tech? And what does it have to say about bootcamps? First of all, tell us what ChatGPT is and how does it work? ChatGPT is a large language generation model developed by OpenAI, based on the transformer architecture. It uses deep learning techniques, specifically a type of neural network called a Transformer, to generate human-like text responses to input prompts. The model is trained on a large dataset of text, which includes diverse types of writing such as news articles, conversation dialogues, and other texts. During training, the model is exposed to many examples of input and their corresponding outputs, and it gradually learns patterns and relationships between the input and output. When the model receives an input prompt, it processes the input through multiple layers of the network and generates a probability distribution over all possible outputs. The model then selects the output with the highest probability as its response. Overall, ChatGPT works by using statistical methods to generate text based on patterns it has learned from its training data, and it does not have the ability to reason, understand, or make decisions in the way that a human would. Tell us about the tech sector: What are the most exciting aspects of working in tech? Working in the tech industry can be an exciting and rewarding experience, offering many opportunities for personal and professional growth. Here are some of the most exciting aspects of working in tech: Innovation and creativity: The tech industry is constantly evolving and changing, providing opportunities for individuals to innovate and create new solutions to complex problems. Variety of work: Tech encompasses a wide range of fields, from software development and data science to cybersecurity and artificial intelligence. This means that there is a wide range of roles and opportunities for individuals to choose from and explore. High demand for skills: The tech industry is growing rapidly, and there is a high demand for professionals with the right skills. This can result in job security, competitive salaries, and opportunities for advancement. Collaboration and teamwork: Many tech roles involve working in teams, providing opportunities for individuals to collaborate with others and learn from one another. Impact: Technology has the potential to positively impact society in numerous ways, and working in tech can provide opportunities to make a difference and help improve people's lives. Constant learning: The tech industry is constantly evolving, and there are always new technologies and techniques to learn. This can provide opportunities for continuous learning and professional growth. These are just a few of the many exciting aspects of working in tech. The field offers a dynamic and challenging environment, and is a great choice for those who enjoy solving complex problems, working with new technologies, and continuously learning and growing. What do you think are the most in-demand skills in tech? The most in-demand skills in tech can vary depending on the industry and current market conditions, but here are some of the skills that are consistently sought after: Cloud Computing: Cloud computing has become increasingly popular in recent years, and skills related to cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform are in high demand. Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are growing areas of technology with applications across many industries, and skills in these areas are highly sought after. Web Development: Web development skills, including HTML, CSS, JavaScript, and React, remain in high demand as the majority of businesses have a web presence and need developers to build, maintain, and upgrade their websites. Cybersecurity: As cyber attacks become increasingly common, there is a growing need for professionals with skills in cybersecurity, including network and information security, threat intelligence, and digital forensics. Data Science: Data science is a field that involves using statistical methods, programming, and data visualization to extract insights from data. Skills in data science, including Python programming and SQL, are highly sought after by companies in a variety of industries. Full-Stack Development: Full-stack development involves working with both the front-end and back-end components of web development. Full-stack developers who are familiar with multiple programming languages and have experience with databases, server-side technologies, and front-end frameworks are in high demand. These are just a few of the most in-demand skills in tech, and the list can vary depending on regional and industry trends. To remain competitive in the tech job market, it is important to stay up-to-date with the latest technologies and to continuously improve and expand your skills. Why should someone consider gaining tech skills? Gaining tech skills can offer many benefits, both professionally and personally. Here are some reasons why someone might consider gaining tech skills: Job opportunities: The tech industry is growing rapidly, and there is a high demand for professionals with tech skills. This can result in job security, competitive salaries, and opportunities for advancement. Career growth: Tech skills are in high demand, and individuals with these skills are often in a strong position to advance their careers and take on more challenging and higher-paying roles. Entrepreneurial opportunities: Tech skills can also be valuable for individuals looking to start their own businesses or become entrepreneurs, as technology plays a crucial role in many startups and innovative businesses. Problem-solving skills: Tech skills can help individuals develop valuable problem-solving skills that are useful not just in tech, but in many other industries as well. Adaptability: The tech industry is constantly evolving, and individuals with tech skills are often better equipped to adapt to changes and new technologies. Personal development: Learning new tech skills can be a rewarding personal experience, providing individuals with the opportunity to challenge themselves, expand their knowledge, and build their confidence. These are just a few of the many benefits of gaining tech skills. Whether you are looking to advance your career, start your own business, or simply learn something new, tech skills can provide valuable opportunities for personal and professional growth. Why choose a tech bootcamp over a university degree? Tech bootcamps and university degrees are two different paths to a career in technology, and choosing between them can depend on a variety of factors. Here are some reasons why someone might choose a tech bootcamp over a university degree: Speed: Tech bootcamps are typically shorter in duration, lasting anywhere from a few weeks to a few months, compared to the several years required to earn a university degree. This can make them a more appealing option for those who want to quickly transition into a tech career. Cost: Tech bootcamps are often less expensive than university degrees, especially considering the shorter duration. This can make them a more accessible option for those who are seeking to retrain or change careers, but are unable to invest the time and money required for a traditional university education. Practical skills: Tech bootcamps focus on teaching practical skills that are in high demand in the tech industry, such as coding, web development, and data science. They aim to equip students with the skills needed to immediately begin working in their chosen field. Flexibility: Many tech bootcamps offer flexible scheduling options, including part-time, online, and evening courses, making them a good choice for those who are unable to commit to a full-time program. Networking opportunities: Tech bootcamps often offer opportunities to network with industry professionals and alumni, which can be valuable in finding job opportunities or making connections in the field. It's important to note that university degrees and tech bootcamps have different strengths and weaknesses, and choosing between them will depend on individual circumstances, goals, and preferences. For example, a university degree may provide a more well-rounded education, with a greater focus on theory and a wider range of course offerings, while a tech bootcamp may provide a more focused and intensive learning experience that is more directly applicable to a career in technology. How is the UK tech market compared to other European markets? The UK tech market is one of the largest and most developed in Europe, and is considered to be a leader in several key areas, including fintech, artificial intelligence, and cybersecurity. Compared to other European markets, the UK tech market has several unique advantages, including: Access to capital: The UK has a well-developed venture capital market, and there is significant investment available for tech startups and growing businesses. Talent pool: The UK has a large and highly skilled talent pool, with many top universities producing world-class tech professionals. Favorable business environment: The UK has a pro-business environment, with favorable tax policies and a supportive regulatory framework for tech businesses. Global reach: The UK has a strong position as a global hub for tech, with many multinational tech companies based in the country and a well-established network of international partnerships. However, the UK tech market also faces some challenges and risks, particularly in the context of Brexit and the ongoing uncertainty around the UK's relationship with the European Union. Nevertheless, the UK tech market continues to be a vibrant and dynamic sector, with many exciting opportunities for businesses and professionals. When compared to other European markets, such as Germany, France, and the Netherlands, the UK tech market has a strong position and is considered to be one of the leading tech hubs in Europe. However, each European market has its own strengths and weaknesses, and the relative competitiveness of the UK tech market will depend on the specific focus and goals of the companies and individuals involved. What's your advice for someone thinking about changing careers and moving into tech? If you are thinking about changing careers and moving into tech, here is some advice that can help: Start learning: The first step in changing careers is to start learning about the tech industry and the specific skills and technologies you are interested in. There are many free online resources, such as online courses, tutorials, and forums, that can help you get started. Network: Building a network of professionals in the tech industry can be a valuable resource for career advice and job opportunities. Attend industry events, join online communities, and reach out to people in your network who work in tech. Gain experience: Gaining hands-on experience is crucial when transitioning into a new career, and there are many ways to do this, such as taking on freelance projects, participating in hackathons, or contributing to open-source projects. Update your skills: Keeping your skills up-to-date is important in the rapidly evolving tech industry. Consider obtaining certifications, attending conferences, or enrolling in continuing education courses to stay current with the latest technologies and best practices. Be open-minded: The tech industry is diverse and there are many different roles and careers within it. Be open-minded about the type of role you want to pursue and be willing to take on entry-level or junior positions as you build your experience and skills. Find a mentor: Finding a mentor who has experience in the tech industry and can offer guidance and advice can be invaluable. Look for someone who has a similar background or interests and who is willing to support you in your career transition. Be persistent: Changing careers can be challenging, but with persistence, hard work, and a positive attitude, you can successfully make the transition into the tech industry. These are just a few of the key steps you can take to change careers and move into tech. By following these steps, you can gain the knowledge, skills, and experience needed to build a successful career in the tech industry.

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    11 Great Jobs in Tech for Creative People

    Juliette Erath - 2023-07-08

    For those who enjoy innovation, a career in technology might be exactly what you’re looking for. Whether you want to develop products for companies or design new-age video consoles, there are many roles for creative people in technology–even ones that don’t involve math or extreme computer knowledge! In this article, we’ll highlight some of the best creative jobs for those who want to pursue a career in tech, but aren’t totally sold on the technical side. Creative Jobs in Tech Creative jobs are specific tech roles that require specialized skills and creativity, combined with technical know-how. Creative roles in areas such as graphic design and digital marketing support companies in creating essential solutions that allow their projects or business to succeed in the competitive marketplace, ensuring their products reach the eyes and ears of diverse audiences. 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    What Is a Tech Lead?

    Juliette Erath - 2023-06-17

    Interested in being your software team’s go-to for technical questions? Dream of becoming a leader in your department? Excited about utilizing new technologies to guide innovation within your department? If these all sound familiar, then tech lead could be the perfect role for you. In this article, we’ll break down the responsibilities and requirements of a tech lead to figure out if it’s the right fit for you. What Is a Tech Lead? By looking at the term “tech lead,” we can gather two things: they work in both a technical capacity and a leadership position. So then, what does a tech lead do? A technical lead – or tech lead for short – oversees the technical aspects of a software team by making architectural and design decisions, guiding team members in technical matters, and supervising system modifications. Some key job responsibilities of a tech lead include: Establishing project specifications and technical direction Creating a framework of technical standards and procedures Overseeing software quality and system modifications Guiding team members with troubleshooting technical issues Analyzing existing operations to identify risks and redundancies Conducting comprehensive assessments to develop solutions In other words, as a tech lead, you’re basically setting the technical direction of the project and clearing the way of any programming hurdles that may arise, whether it be closing technical knowledge gaps, finding answers to product questions, or streamlining existing operations. As a tech lead, you'll engineer, guide, and implement technical solutions and improvements with the help of your software development team. You can get your hands dirty and do some coding once in a while, but that isn’t your main job responsibility. So, if you’re someone who enjoys both the technical and interpersonal aspects of software development, a tech lead may be the job for you. With Whom do Tech Leads Work? Together with team leads and product owners, tech leads play a leadership role in a software or engineering department; each leader focuses on a different aspect of the production process. A product owner will focus on overall product development and a team lead will focus on people development; a tech lead focuses on technical development. Together, they set the project’s direction and guide the team members to ensure the project’s deliverables are fulfilled. Some essential members of the software development team include: Product Owner : leads product development to fulfill client’s requirements Team Lead/ Engineering Manager: guides team through development process Project Manager: oversees the development, organization, and delivery of a product Software Developer : designs and develops software to fit the users’ needs Quality Assurance Engineer: confirms developed solution fits specifications Software Tester: executes tests to ensure solutions fit performance and security demands UX/UI Designer: designs features to enhance customer experience and interaction A software team works together to ensure the smooth delivery of a product;as a tech lead, you must be able to balance priorities, communicate clear goals, and make apt decisions , all while taking into account your team’s capabilities and the product’s deliverables. Since each position on the team has some involvement in the technical operation, you must make sure that each team member has a handle on their portion of the software development process to ensure the delivered product meets the client’s expectations. What Skills Do Tech Leads Need? A tech lead can be considered a leader, developer, and architect all in one, which defines their essential skill set: As a leader , they supervise team members, delegate tasks, issue feedback, evaluate risks, and resolve conflicts. As a developer , they apply programming tools, utilize design patterns, perform automated testing, and refactor code. As an architect , they perform technical assessments, oversee development lifecycles, direct release management, and adhere to federal regulations. As you can see, tech leads must not only have the technical expertise to develop products, but have the people skills to lead teams as well. Some crucial skills of a tech lead include: Communication and leadership Project and crisis management Software architecture and systems testing Technical guidance and decision-making skills Quality assurance Problem solving and innovation Ownership and vision How Can I Become a Tech Lead? You can develop these skills by gaining professional experience along with taking technical and leadership courses. A tech lead isn’t an entry level role; starting off, tech leads often have 5+ years of experience in the field and a bachelor’s degree. But remember, a tech lead also isn’t just a senior software engineer: just focusing on programming knowledge is not going to get you there, you need to build interpersonal skills as well. So, how can you do that? Well: You can apply some professional approaches , such as supporting coworkers, providing technical advice, suggesting new initiatives, creating innovative solutions, and requesting performance-based feedback, to show your resourcefulness while developing new skills. You can use some administrative tools , such as a strengths and weaknesses chart, a professional development plan, and a goal breakdown, to guide your learning and development while focusing on the professional skills you need to reinforce. With a proper plan of attack, you can be well on your way to becoming a tech lead. As you know, the job title has the term “lead” in it, which means it’s considered a senior role – but still a great target position. Since tech leads are considered to be both technical experts and competent supervisors, you’ll need a decent amount of experience first. Our Web Development Bootcamp is a great first step that you can take to establish the essential hard and soft skills needed to form a strong foundation in web development that you can build on with professional experience. Web development has boundless possibilities, with a tech lead being one of them. We can’t wait to see where you go next!

  • 8 minutes

    How to Actually Use ChatGPT 

    Ironhack - 2023-04-17

    You’ve probably heard a lot about ChatGPT recently. Maybe you’ve read one of our blog posts, come across an article in a major newspaper, or even seen a post on one of our social media pages. Lately, it seems like ChatGPT is all everyone is talking about. And with all the hype around ChatGPT, there’s never been a better time to learn how to use it effectively. But before we can do that, let’s talk about ChatGPT: what it is and (most importantly!) what it isn’t. What is ChatGPT? In ChatGPT’s own words, “ChatGPT is a large language model based on the GPT (Generative Pre-trained Transformer) architecture. As a language model, ChatGPT is designed to generate human-like responses to natural language input, allowing it to converse with users in a way that resembles a human conversation.” Let’s break that down a little. What we’ve just read is that ChatGPT is a large language model that accepts inputs in plain English (or any number of languages, for that matter) and generates responses similarly to how a human would. But–what exactly is a “large language model?” In brief, it’s a type of artificial intelligence that is trained on human-generated text data in order to be able to understand natural language patterns. In simpler terms, it’s a machine learning model that is fed massive amounts of text so that it’s able to (1) understand text inputs and (2) generate outputs that it calculates are likely to make sense given a specific input. Here, “generate” is a very carefully chosen word. ChatGPT is generative , which means that it can create outputs that it’s never seen before. ChatGPT does not just repeat text that was in its training data; it is able to extrapolate from its training data to create brand new words, sentences, and ideas. So, great! Now we know exactly what ChatGPT is. However, we also need to know what it isn’t before we are able to effectively use it. What are ChatGPT’s limitations? It might seem like an all-powerful beast, capable of absolutely everything and on the verge of taking over the world. But don’t get too ahead of yourself: ChatGPT is not able to do everything. Yes, it’s a powerful tool when used effectively, but it can’t quite replace a human being. Like many other great tools, it can be used to tremendously enhance productivity by helping its user brainstorm ideas or automate tasks, but it’s bounded by these four factors: A dependence on data quality A lack of common sense An inability to handle complex tasks Limited domain expertise What Can I Do With ChatGPT? Now that we have a better understanding of what exactly ChatGPT is, it’s time to learn about what we can actually do with it. In short, you can ask ChatGPT to do anything that can be answered as text and we can list them under three broad categories: explanatory, conversational, and generative. Explanatory : you can ask ChatGPT to explain a word, a concept, or an idea. You can also ask ChatGPT to summarize, re-word, translate, or otherwise clarify a body of text. Example: What does the word “engage” mean? Why is the sky blue? Why do dogs sleep on their backs? Conversational : ChatGPT can engage in a conversation with you, answering questions, providing suggestions, and helping with decision-making. Example: Can you give me some ideas for a child’s birthday party? What should I get my partner for our 10 year anniversary? What is the best way to arrive at my destination? Generative : ChatGPT can generate content such as articles, stories, or code. This can be useful for brainstorming or content creation, but remember to always review and edit content before using it, as the quality of the generated content may vary. Example: Create a blog post about automated processes. Write code that fits into this project. Can you give me content ideas for my startup? How Can I Effectively Use ChatGPT? Whew! By now, you’ve already learned a lot about ChatGPT and are probably itching to start using it. In this next section, we’re going to quickly go over how to use ChatGPT effectively so that  you can get the most out of this powerful tool. We think that it only takes 6 steps to use ChatGPT to its full potential; you should clearly define your task, use appropriate prompts, verify the response from ChatGPT, check the response for biases, edit the content in the response as needed, and then start to experiment! Clearly define your task Before ChatGPT can help you, you need to know exactly what you’re looking for . In other words, you have to know what you don’t know. Once you have a clear understanding of what you need, you’ll be able to generate a prompt that accurately conveys what your task is to ChatGPT. Remember, ChatGPT can’t read your mind! You need to tell it, in writing, exactly what you want. Use appropriate prompts Once you’ve defined your task, you are then able to write a prompt that can enable ChatGPT to generate a relevant response. In your prompt, you should use keywords that help ChatGPT understand what kind of response you’re looking for. Some examples of keywords are: Topic : this one’s pretty obvious, if you’re looking for information on a certain topic, be sure to include keywords related to the topic in your prompt. For example, if you’re looking for information on renewable energy, you should use the keyword “renewable energy” but also related keywords such as “zero-emissions” and “solar power.” Question : if you’re looking to answer a specific question, it’s good to give ChatGPT more context on the nature of your question: are you asking about a who, what, why, when or how? Action : ChatGPT can perform several different kinds of actions, but you need to tell it what to do. Some keywords that may be useful are “summarize,” “define,” “explain,” “analyze,” or “generate.” Comparison : if you want to know more about how two things compare, you should include keywords such as “advantages,” “disadvantages,” “benefits,” or “contrast” so ChatGPT knows how best to frame the comparison. Location : if the information you’re looking for is specific to a region or a timeframe, be sure to include that in the prompt. For example, if you want to learn more about the history of renewable energy in the US, you can ask something like, “In the last 50 years in the United States, …” For best results, it’s important that your prompt be as specific as possible . When you leave room for interpretation, you also leave room for the generated response to be less accurate or relevant to your needs. Verify the response Your job isn’t done as soon as ChatGPT generates a response, however. Once you have the response, you should go through the steps necessary to validate its veracity . It’s not always easy to check if information is true, but it’s incredibly important that you do so before using it or else you risk damaging your own credibility. We recommend you take the following steps: Fact-check the information to quickly determine if it’s inaccurate. Websites like FactCheck.org , Snopes , or the Washington Post Fact Checker are all credible. Cross-reference the information you received from ChatGPT with information you’ve found in multiple other sources. Two sources is the minimum for verifying information is true and you should use more if possible. After you’ve found multiple sources confirming the information is true, you still have to confirm that those sources are credible . Plus, you should also make sure that your sources don’t cite each other. If they do, then they only count as one source. If you’re still unsure about the information, consult an expert in the field ---they’ll be able to provide additional insight and give credible sources to back up their claims. Check for biases This next step may be harder to do without context or expertise, but is nonetheless important. Once you’ve determined a response is factually correct, you should take the time to examine if it contains any potential bias. Using information with external motives or incentives without disclosing them may be just as damaging to your reputation as using false information. Edit the content We’re almost done! At this point, we’ve already determined what we’re looking for, crafted an appropriate prompt, received a response, and checked that response for biases and errors. Now, it’s time to (1) correct those details and (2) edit the content to fit your particular needs. Depending on what kind of content you asked for, you may want to copy the response to a text editor such as Google Docs or a code editor such as Visual Studio Code. Next, you may want to use a tool such as autocorrect for written content or a linter for code to help you identify obvious grammatically and structural errors. At this point, you should choose whether you want to do a full re-write or whether you’ll just be fixing the errors in the generated response. Finally, once you’re done, make sure to do one last read-through before publishing the content. Experiment As with most things, you’ll get better at using ChatGPT with time and practice. The more things you attempt and experiment, the more you’ll learn and improve! You’ll be surprised at how quickly you can improve your prompt engineering skills by just trying different prompts and tasks. Over time, you’ll develop a better understanding of how to use ChatGPT effectively for your specific needs. Where do we go from here? If you’re looking to not just become a more proficient ChatGPT user but also actually break into the world of tech, check out our bootcamps. There are bootcamps available for just about anybody, and we think that you’ll surely find something that’ll help you grow in your career. Poke around our website and see which bootcamp is the best fit for you. We can’t wait to see you in class!

  • 7 minutes

    Google Bard: What it Means for You

    Ironhack - 2023-06-02

    Everyone’s heard of ChatGPT, but that’s certainly not the only option when it comes to free, publicly-available, generative large language models . If you work in tech, we think you should heavily consider using Google Bard, especially now that they’ve updated the platform to be able to generate code. But, that’s not the only thing it can do—the Google Bard AI directly integrates with many other Google services, allowing you to directly export code to Colab, export a summary to Docs, or even send a request to gmail. How to Get Access to Google Bard First things first! Before you can use Google Bard, you need to meet just three basic requirements: Have a personal Google account Be 18 years or older Use Chrome, Safari, Firefox, Opera, or Edgium If you meet all three requirements, then just navigate to https:/bard.google.com and click “Sign in” in the top right corner. Earlier this year, there used to be a waitlist in order to use Bard, but we haven’t encountered any waitlist in recent tries. With that said, if you do end up on the waitlist, don’t worry; in our experience you’ll be granted access in the next 2-3 days. If you don’t meet all the requirements, there’s still a chance you can use Google Bard. Let’s go over solutions in order: If you don’t already have a personal Google account ( where have you been?! ), you can make one right now by clicking this link . If you’re not 18 years or older, then unfortunately you’re out of luck. You can’t use Google Bard with a Google account managed by Family Link or with a Google Workspace for Education account designated as under the age of 18. This is a strict requirement, and they don’t make any exceptions. If you don’t normally use a Chromium-powered browser (or Safari), Google will deny you access to Bard. Like Microsoft with their AI-powered Bing, these companies want you to use their own software to gain access to “experimental” features. Fortunately, browsers are super easy to download and, when in doubt, download Firefox. What is Google Bard? Now that you know how to access Bard, we’re sure the next big question on your mind is: “What does Bard stand for, Google ?” Well, we’re here to answer all questions, no matter how big or small. Google doesn’t usually publicize why it names products the why it does, and this time is no different--but we’ll take a crack at it anyways. A bard is an old English word for a poet or storyteller---someone who creates art with language. Since Google Bard is a generative large language model , the name “Bard” is a play on the fact that this is software that creates responses using natural language. Google Bard vs ChatGPT There’s two big players in the generative language model space: ChatGPT by OpenAI and Bard by Google. But hold up, we know what you’re thinking—what about AI-powered Microsoft Bing? Isn’t that another chatbot that’s super similar to Google Bard? Yes, you’re right, they are similar, but, AI-powered Microsoft Bing actually uses ChatGPT 4.0. Google Bard and ChatGPT have a lot in common: they’re both generative large language models and can be used to do similar things from simple chatbot-like tasks to more complex tasks like generating code. Here’s how the two products match up head-to-head: Bard: Was created by Google Uses the Language Model for Dialogue Applications (LaMBDA) Sources dataset from Common Crawl, articles, books, Wikipedia, and real-time searches Is free ChatGPT: Was created bv OpenAI Uses the Generative Pre-Trained Transformer (GPT) Model Sources dataset from Common Crawl, articles, books, Wikipedia, and real-time searches (only available on GPT-4, which is paid). GPT-3.5 is free, but users must pay a subscription to use GPT-4 While Bard was released after ChatGPT, Google has a lot of experience with natural language processing from work associated with improving Google Search and Google Assistant. In fact, Google Research invented and open-sourced the neural network architecture that’s used by many language models, including GPT 3.5 and GPT 4. How to Use Google Bard AI Now that we know all about what Google Bard is and where we can access it, we should probably tackle the question, “ Well , how do I use Google Bard ?!” You’re in luck! There’s no right or wrong way to use products like Bard. It’s a language model that’s built to have human-like conversations; if you can give it a task in words, it can probably do it (or at least help you accomplish it yourself). But, we know that’s really broad—it’s probably easier to understand if we split the things that Bard can do into three categories: explanatory, conversational, and creative. We’ll give a brief explanation of each category as well as an example question or task along with a snippet of Bard’s answer. Explanatory : Bard can explain all kinds of things from giving the definition of a word to answering challenging and open-ended questions about life or the universe. Q: “Who painted the Mona Lisa?” A: “The Mona Lisa was painted by Leonardo da Vinci, an Italian polymath of the High Renaissance who was active as a painter, draftsman, engineer, scientist, theorist, sculptor and architect.” Conversational : Bard can engage in a conversation with you on practically any subject. It’ll remember your responses and be able to impress you with how it can handle complex topics thoughtfully. Q: “You are a web developer who was teleported to the 18th century. What's the first thing you would do?” A: “[The] first thing I would do is try to find a way to communicate with people from my own time…” Creative : Bard can create poems, narratives, musical pieces, emails, and even code. Q: “Write a haiku about desert sunsets.” A: “Sunset blazes red, Ashes of day fade to night, Desert sky aglow.” As you can see, Bard can do basically anything you ask of it—but it does have several limitations. Let’s discuss: For starters, Bard does not fact-check itself when giving answers. We cannot overstate this enough: generative AI should never be used as a source of truth . For example, in the “creative” section above, we showed Bard’s answer to the prompt, “Write a haiku about desert sunsets.” In the answer (and feel free to check for yourself), Bard gives us a poem with three lines of 5, then 7, then 5 syllables. This is great! It’s indeed a haiku; however, that was not Bard’s initial answer. This is what we originally received: Sunlight fades to red, The desert sky ablaze with color, A perfect end to day. If you count the syllables in this poem, we have 5, then 9, then 6 syllables in the lines, which is not what we asked for! Ouch, a Google Bard error. Only after we informed Bard that the second and third lines had too many syllables did it come back with a poem in the correct format. Recent Updates As we’ve already mentioned, Bard can do basically anything you ask. Obviously, it’ll do some things better than others—but it’ll always try its best. And, Google is continuously updating Bard so that it’s able to do more things. Here are a few notable changes: Bard can now generate code in languages such as C++, Go, Java, and Python When generating Python code, you’re able to export the code directly to Google Colab Bard can export content directly to Google Docs and Gmail Bard can cite sources directly in the response Bard can display images What’s Next? Bard is Google’s most recent product release and we think it’s also one of the coolest project’s in the company’s history. Google has been working on generative large language models for decades now, and it’s great to finally see and interact with a product that’s the culmination of so much time, effort, and care. If you’re interested in working in the tech industry and creating awesome products that help make the world a better place, then we think you would be a perfect fit for one of Ironhack’s bootcamps. We offer bootcamps for just about every role in tech, and there’s never been a better time to join the industry. Want to learn how to make the most of ChatGPT, Google Bard, or other generative language models? We can’t wait to see you in class!

  • 10 Best Tech Companies To Work For And Why

    Ironhack - 2022-02-04

    [This article was updated for 2022.] It's a dream for many people to work in a renowned tech company. Thousands of tech enthusiasts seek to get their foot in these companies as they are known to offer excellent perks and professional growth opportunities. Equally, their reach attracts talent from different parts of the world. You don’t need a full Computer Science degree to get hired by these companies, though. Bootcamp alumni have a unique combination of skills and assets and the value they bring into the workplace increases exponentially for years– which is why companies are now turning their attention towards bootcamps. While tech companies have very different products and look for a variety of profiles, some paths in technology and computer science are growing in demand . Let’s look at some of the top tech companies that invest in talent in the competitive tech world. 1. HubSpot Renowned among digital marketing companies, HubSpot offers a wholesome platform of sales, marketing, customer service, and CRM software to cater to small and large businesses alike. HubSpot values its employees' input the same way they value their customers. Many employees express the company's willingness to assist and work with skilled people while also providing opportunities for career growth. If you can comfortably work under strict deadlines and high expectations, HubSpot is an excellent organization to help you achieve your career goals. Great for: startup-lovers with an interest or experience in digital marketing. 2. NVIDIA All employees who show dedication and hard work to advance their careers in tech will receive abundant rewards securing employment at NVIDIA. Influencing changes in the tech world and working on trailblazing technology are some of the reasons most people desire to work at NVIDIA. Plus, most employees enjoy the work environment and the organization’s strong emphasis on performing tasks as a team. At NVIDIA, you may need to put in some extra hours, especially when you join the company and seek to advance. There’s high competition, and it therefore helps when you show initiative and commitment in the respective roles. Great for: lovers of all things visual, especially in the gaming and CGI spaces . 3. Salesforce Companies like Salesforce provide businesses with marketing tools and customer management strategies. Employees appreciate its culture of excellent benefits, programs, and events that make their contributions valued. The only downside is that programs and events don't always make employees feel valued. Some employees complain of long work hours regardless of position within the company. Great for: data-nerds, who believe in empowering teams through data and smoother workflows . 4. DocuSign While there are emerging competitors in the market, DocuSign is a trusted company that allows you to sign documents electronically. DocuSign have a notable competitive edge since they manage to offer benefits to employees. More employees appreciate the positive morale and work/balance that’s the foundation of the company's culture. With a high Glassdoor rating, the company invests in all its employees to succeed long term. However, some employees state that DocuSign has a confusing onboarding process where management makes hasty changes. Great for: people who highly value a company that invests and believes in its employees. 5. Shopify Shopify is a dedicated company that helps businesses sell multiple products online. It has a good Glassdoor rating compared to other tech companies as a loyal employer. Overall, the company has a commendable employee culture, and they provide good benefits and promote employee events. Even so, some employees feel the company needs to value their input and involve them more in decision-making. Great for: those looking to break into the exciting world of eCommerce, it doesn’t get better than Shopify! 6. Google Google is the most sought-after firm by jobseekers. The search company continues to excel in providing better employee benefits. Working in a tech giant like Google gives you a chance to interact with the brightest talent pool in the world. And to stand apart from the competitors, Google offers attractive benefits to employees that most companies won't match owing to their vast budget. The only challenge of working at Google is having incredibly high expectations concerning loyalty and performance. Other challenges include the unfair competitive hierarchy that limits advancement potential. Great for: impact-chasers who want the work they do to improve the lives of millions (literally!) . 7. Facebook/Meta As the leading social media company, Facebook/Meta aims to create a healthy work environment by treating its employees as assets. Aside from having a superior facility design, the organization has attractive perks for its employees—from 401 contributions to pizza parties. That said, you might have to sacrifice your time as most company positions entail long working hours and a competitive atmosphere. Great for: problem-solvers and future-thinkers, who aren’t afraid to tackle world-changing topics like data use and the future of the Metaverse . 8. LinkedIn LinkedIn has become a valuable tool to connect colleagues professionally, providing businesses with a unique social media podium. The company is making great strides in keeping employees valued and appreciated with a commendable rating. LinkedIn is the best company in the tech world due to its leadership role. Plus, employees state that the company promotes a healthy work culture. Additionally, employees receive many attractive benefits and team-building events that foster teamwork. Great for: anyone passionate about building the future of work, in all of its new forms! 9. Adobe Adobe is famous for its collaborative design interface that allows for superb graphics. The company treats its staff well. The organization offers competitive benefits and focuses on cultivating a healthy work culture based on rewarding commitment and loyalty. However, some employees feel that the company has a lot of bureaucracy that hinders meaningful progress. Advancement openings seem to target the selected few, and employees can sometimes feel like they’re stuck in one position for an extended period. Great for: creatives and visionaries who get excited about what people can make when you hand them the right tools . 10. Microsoft Working for Microsoft is a great achievement as the tech firm is renowned worldwide. It creates most of the computer operating systems that exist today. Microsoft stays dedicated to manufacturing innovative products to solve the industry’s pain points. Employee retention is essential to maintain that edge, where the comprehensive benefits package fosters innovation. Employees also enjoy working in a competitive environment with the most skilled personnel globally. Great for: anyone with a deep love of tech, and the impact this tech giant has had on the industry– and anyone who is even more excited about the future it could build… How To Land Your First Tech Job Working for a prominent company is an achievable, yet challenging goal, if you’re trying to break into the tech industry. But you can land a job in tech even if you’re new to the field. Multiply your odds by getting an education that will help shape your profile into one that tech recruiters are looking for. Bootcamps are about so much more than just learning the core technical skills you need; they show that you’re a dedicated, self-motivated learner with a diverse professional background, which is precisely what tech industry hiring managers are looking for. Piqued your interest? Take a look at our Bootcamps and kickstart your career in tech!

  • 4 minutes

    Campus Spotlight: Miami

    Juliette Erath - 2023-03-09

    Miami is known for its sunny days, multicultural community, and sandy beaches. Located in southern Florida, Miami brings together a wide range of cultures and countries with its extremely diverse population. Thanks to this diversity, Miami is one of the most popular destinations in the US and the entire world . With a wide range of shopping opportunities, incredible nightlife, outdoor activities abound, and lots of sunshine, there’s no doubting why so many people choose Miami to live, work, and study. Why Miami? Miami plays a unique role: it connects Latin America to the US, ensuring that the city is extremely diverse and full of different cultures. Take for example the Wynwood Arts District , a hipster neighborhood filled with galleries, shopping, tech companies, great restaurants, and nightlife. Or the Brickell neighborhood , a metropolitan area filled with high rises, large businesses, tech companies, and restaurants. Both of these offer tech happy hours consistently that help newcomers connect and network with the bustling Miami tech community. Following the pandemic, lots of workers saw the possibility of remote work and moved towards Miami’s sunny beaches, making it a new powerhouse in tech. Miami’s tech scene raised close to $1 billion in venture funding in 2022 and is ranked as the #1 city in startup activity . Is that not enough?! Check out these highlights: Year round tech and Web3 conferences : eMerge Americas is one of the largest tech conferences in the country. A wide variety of industries : Miami is known for tourism/hospitality, maritime, and real estate (all industries that need tech workers). Up and coming tech hub : since the pandemic, many tech companies based in California and New York have opened offices in Miami. Rise in Web3 activities : there has been a rise in Web3 activities as Miami’s mayor has bet on the future of the blockchain. This has helped many tech groups, conferences, and startups build a future in Miami, providing our students with great networking opportunities. Ironhack in Miami Ironhack Miami looks to harness the region’s potential and offer in-person and remote courses for those looking to dive into tech in the US. Boasting lots of events to connect students with local tech leaders, you will be well-suited for success. Here’s why: We’re certified by the Florida Department of Education We have a Women in Tech Collective , where we host events with community thought leaders and discuss ways to close the gender gap Our financing partner is Climb Credit We serve the Tri-County Area (Miami-Dade, Broward, and West Palm Beach) as well as the whole country Many students from neighboring counties take the new Brightline train for our in-person offerings or do the remote program Ironhack courses in Miami Web Development : learn front and back end technologies and everything else you need to know to become a full stack web developer. Data Analytics: become a data analyst through learning tools such as Python, SQL, and Tableau and learn to interpret data to make the right decisions. UX/UI Design Uuser-centric design is key here; learn design thinking to build digital experiences created specifically for users. No matter if you choose remote, full-time, or part-time courses in Barcelona, Ironhack’s campus offers you something beyond your course; we offer events, chances to network and connect with your fellow Ironhackers, an awesome space to get your best work done, and much more. Unsure if you should take the full-time bootcamp? What about part-time? Or remote? Let’s discuss: Our full-time bootcamps are ideal for those looking to get a new job soon and can exclusively dedicate nine weeks to learning. If you want fast results, this is for you. Our part-time bootcamps are for those who can’t dedicate nine weeks to just the bootcamp; if you want to keep your job elsewhere or continue your studies in another institution, our part-time bootcamps are perfect for you. The results are the same as our full-time course, just spread out over more weeks to allow you time to handle your other commitments. Our remote bootcamps are an option for everyone, even if you’re already located near our campus. These courses offer more flexibility and start dates, in addition to language variations, to help you choose the perfect course for you. And don’t worry about missing out on our events or course help; remote students are welcome at campus any day of the week and can benefit from everything offered to in-person students. Financing options in Miami As part of our dedication to making our courses accessible to all, we’ve partnered with ClimbCredit to offer funding opportunities to students. Full-Time programs Upfront payment : pay the entire course upfront by debit card, credit card, or bank transfer. Interest-free recurring payments : Make four recurring payments of $3,000 by debit card, credit card, or bank transfer over the course of four months. Climb loan : this monthly loan payment plan allows you to pay $67 to $115 for the first three months and then pay it off over 36 monthly payments. There are available co-borrower options and the final terms are subject to approval. Applying does not affect your credit score. Deferred climb load : pay $0 for the first 6 months and then pay it off over 36 monthly payments. Interest accrued over the first six months will then be added to the payment over the next 36 months. Applying does not affect your credit score. Part-Time programs Upfront payment : pay the entire course upfront by debit card, credit card, or bank transfer. Interest-free recurring payments : make seven recurring payments of around $1,786 by debit card, credit card, or bank transfer. Climb loan: take advantage of reduced payments of $7 to $131 for the first 3 months and they pay it off over 36 months. There are available co-borrower options and the final terms are subject to approval. Applying does not affect your credit score. Does Miami seem like the right fit for you? If you are eager to take advantage of all it has to offer and jump into your new tech career, we’re ready to hear from you. What are you waiting for?!

  • 5 minutes

    How to Begin a Career in Cybersecurity Without Previous Knowledge

    Ironhack - 2021-05-04

    We’ve all felt the effects of when companies don’t make cybersecurity a priority. You might even have had to bear the brunt when companies don’t invest in their cybersecurity yourself: personal data gets leaked, sensitive information is spread across the web, and life savings can be lost if you give the wrong company your bank details. You’re probably sick of this happening and we get it. That’s why it’s time to become a cyber warrior and kickstart your career in cybersecurity – even if you don’t have any previous knowledge. Cybersecurity may seem complicated to those on the outside, mostly because it comes with a dictionary’s worth of jargon. But anyone with a little technical knowledge and the desire to learn can launch themselves into this exciting career path. But that begs the question…how do you go about getting a cybersecurity job when you’ve got no experience? Before we answer that burning question, let’s first break down exactly what cybersecurity is. What is Cybersecurity? First things first: let’s truly understand what cybersecurity is, as it’s never been more important than it is today. With many companies shifting to a remote/hybrid working model and the vast expansion of the online space, there is an increasing need for businesses to invest in IT security and protect themselves from threats such as hacking, data compromise, and identity theft. Technology is getting smarter, but that means that hackers and cybercriminals are too. The range of cybercrimes is widening rapidly, but some of the most common are: Cyber-extortion : demanding money in exchange for not hacking, damaging, or deleting information Ransomware attacks : locking or limiting access to data in exchange for money Identity fraud : pretending to be you to gain access to your personal information IOT hacking : accessing or manipulating IoT devices and servers Malware : software designed to steal or copy a person’s information Phishing scams : sending fraudulent emails, pretending to be someone else to gain bank or personal information What Does a Career Path in Cybersecurity Look Like? With a growing demand for trained professionals, the cybersecurity industry has a lot to offer prospective employees. Salaries in this field tend to be lucrative and there is ample opportunity for career growth and development. The demand for roles in this area is also set to increase rapidly over the coming years and decades . As such, there has never been a better time to explore cybersecurity and explore the various career options out there for cybersecurity newcomers. There are many areas for specialization and advancement which you’ll be able to branch into, depending on your skills, experience, and career goals. And as the digital world is always changing, new areas that we can’t even imagine today will inevitably reveal themselves! Take a look at some positions, depending on your experience level: Entry-level positions include roles such as Cybersecurity Specialist or Technician, IT auditor, and Incident Responder (anyone interested in cybersecurity for beginners should research these roles as a starting point!) Mid-level positions include Cybersecurity Consultant and Penetration tester Advanced level positions include Cybersecurity Engineer and Managerial positions There are various roles available at each level when it comes to IT security, with plenty of scope for development further down the road; the demand for training in cybersecurity is rapidly on the rise, meaning you may like to branch into this area in a company for which you already work, or that you are looking to pivot completely into a new career. What Does it Take to Break into Cybersecurity? The first thing you need to tackle when it comes to cybersecurity is the basics of IT systems and networks ; for example, the different types of available networks and their protocols. Once you are familiar with the fundamentals, you can dive into the basics of networking traffic, security, and communication principles. Learning how to create and analyze network servers is also important when it comes to cybersecurity for beginners, while data decryption and encryption are also useful, along with topics such as backup processes and data recovery. Discovering how to prevent hacks is also a crucial subject when it comes to cybersecurity for beginners. As technology advances and the online space grows, there are greater opportunities for cyber attacks and hacks to occur, especially when it comes to mobile payments, e-commerce, and cloud computing. Machine learning, social media, and the vast array of apps that many of us use on a daily basis make it easier for criminals to find ways to hack systems to steal data and financial information. The risk for businesses is especially high and this is where cybersecurity professionals come into play : building and maintaining secure systems is essential for most organizations and businesses in the modern world, and expertise in this area is heavily in demand. Breaking into cybersecurity through a bootcamp Courses or bootcamps in cybersecurity for beginners will prepare you for roles such as incident responder, forensics analyst, and cybersecurity analyst, among others. Over time and depending on the size of the company you work for, you may have the chance to branch into a management role, taking control over a cybersecurity team. Similarly, you may choose to specialize in a single area of cybersecurity in networking, software development, systems engineering, or risk analysis. Breaking into cybersecurity with no previous experience Cybersecurity is an exciting and ever-changing career path, with high stakes and high rewards. The demand for professionals in this field is extremely high; there are many opportunities available for anyone interested in developing a career in IT security. But, as with any job, getting started can feel like a huge wall to climb if you don’t have any previous experience. But don’t worry: regardless of your background and previous experience, transitioning to a job in cybersecurity is very achievable with some foundational training, thanks to the recent rise of cybersecurity. The best place to start is with a cybersecurity bootcamp for beginners that covers the basics and the main areas of IT security and data protection. With Ironhack's Cybersecurity Bootcamp, you don’t only get the foundational knowledge and hands-on experience that you need to succeed in your first cybersecurity role; but we’ll actually support you in finding that role. Our Careers Services and Outcomes Team are rockstars at getting you job-ready, helping you figure out the messy middle of job hunting. Why not check it out for yourself?!

  • 6 minutes

    The Perfect LinkedIn Profile for a Data Analyst 

    Juliette Erath - 2023-05-02

    Your LinkedIn profile may seem like a minor part of landing that dream job or even your first role in the industry, but it’s actually an incredibly essential part of your job search. In fact, most recruiters or potential managers first look up candidates on LinkedIn to get a quick summary of their experience, essence, and skills. But before we tackle the specifics of creating that perfect data analyst profile, let’s dive into LinkedIn profile basics that all professionals, no matter their speciality, should master. LinkedIn: Why Does it Matter? You’ve already sent your CV, cover letter, and maybe even a portfolio and references to your potential employer, so why does it matter if you have a LinkedIn profile? Let’s take a look: LinkedIn has more than 600 professional profiles ; it’s an incomparable source for networking, finding connections, and much more. In addition to its personal profiles, it has a job board where tons of companies post all kinds of roles daily. It offers you the possibility of creating your own personal brand with the option to post and promote your own content or website. Thanks to its well-known abilities, it ranks incredibly high on Google , meaning that when your name is searched online, your LinkedIn profile will be the first thing to appear. It boasts a wealth of information : are you interested in checking out a company? Or a potential manager? What about where offices are located? In just one place, you can find out all of this–and more. Tips and Tricks for All LinkedIn Profiles Now that you’re convinced of all the glory of a high-quality LinkedIn profile, let’s tackle exactly how to create that. Uploading your Facebook profile picture to your LinkedIn profile and filling in your employment history is a great start, but there’s a lot more you could do to make your profile stand out and catch the eye of recruiters: Choose the right pictures: we get it; it’s easier to take the picture from your brother’s wedding and use that as your LinkedIn profile picture, but remember: LinkedIn is a professional social networking site and your profile picture should reflect that; choose a headshot that clearly shows your face, is recent, professional, and close-up. But that’s not all: LinkedIn now allows you to include a background photo–choose a photo that describes you and what you offer and contributes to the creation of your personal brand. Make the most of your headline : defining yourself as your job title is easy, but can limit your future opportunities. Instead of just writing your current job title, try to make your headline all-encompassing and able to meet a wide range of roles. Create a captivating summary : LinkedIn offers the possibility of creating a summary where you can write whatever you want; take this chance to stay away from conventional buzzwords like “leader,” “focused,” “experienced,” and “passionate;” these words don’t differentiate you from other candidates. Use these few lines to show recruiters and potential employers who you really are and how you stand out. Include all your experience: as interdisciplinary experience becomes increasingly sought after, employers want to see that you’ve worked in different areas and can handle different tasks or even topics; include all your abilities and accomplishments, no matter how unrelated they may seem. Now that you have the basics down, it’s time to jump into why we’re here: creating the perfect LinkedIn profile for data analyst roles. LinkedIn Profiles for Data Analysts As tech gains importance in every single industry, highlighting your hard skills and versatility is becoming increasingly crucial for potential data analysts. Detailing all your experience and certifications, in addition to specific, actionable results , will help show prospective employers that you’re uniquely qualified for their role. Certifications The vast majority of employers look for candidates with certifications that back up their knowledge; displaying these prominently on your LinkedIn profile helps employers see that you have proof of your skills. Some of the most common certifications for data analysts include the Google Data Analytics Professional Certificate, the IBM Data Analyst Professional Certificate, the Microsoft Certified: Power BI Data Analyst Associate, and the AWS Certified Data Analytics. Projects In addition to your education and certifications, employers want to see specific projects and experiences; add projects (and tag team members) with statistics to show that not only you’ve been successful, but have the data to back it up: Use action verbs : “developed,” “led,” and “initiated” are all verbs that transmit more confidence and expertise than “did” or “completed.” Include data : read these two sentences and let us know which is better (hint: it’s the second!) ; it’s crucial to convey confidence and use data to back up your claim: Led data reduction project. Initiated and directed a project that resulted in a 86% reduction in data. Summary As we mentioned above, your summary is a unique opportunity to provide a recruiter or potential employer with a glimpse into you and what you can bring to the table. Edit your summary to make it fit your personality, but here’s an idea: [NAME] is a data professional with four years of demonstrated experience in the field, using their various certifications and experience at [COMPANY NAME] to deliver a 40% increase in revenue, a 89% decrease in data usage, and never before seen levels of user privacy. [NAME] is skilled in programming, SQL, data management, statistics, and machine learning, using their years of experience to provide an unparalleled data experience to all. Recommendations and endorsements You can list all your achievements, but what truly sets a high-quality profile apart from others is recommendations and endorsements; these are provided by past or current colleagues and are either personalized comments left on someone’s profile or endorsements about specific skills . For example, your previous boss could endorse your language skills, proficiency in Excel, or your teamwork skills; a recommendation would be a few sentences written specifically for you. Both provide an additional layer of confidence to potential employers and can help convey even more trust. It may seem daunting at first, but LinkedIn is your friend and there to help you reach new heights, harnessing both its networking capabilities and capacity for you to show yourself off to help you make new connections and land that perfect role. And no matter if you’re just starting off or have years of experience in the field, LinkedIn is an incredibly useful tool for data analysts.

  • Data Analytics Is Changing The World - Here’s Why You Should Care

    Marta Aguilar - 2023-07-05

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

  • 6 minutes

    What's Going on with FAANG Right Now? 

    Ironhack - 2023-01-17

    With a career in the tech world, you have to stay up to date with everything that’s going on around you in the world of computers, social media, and IT. This includes knowing about the current status of the economy and the stocks that could affect the tech companies that you are working for. It’s an important part of the job because it can potentially affect your salary , the atmosphere of the workplace, and the safety of you and your coworkers’ positions. Right now, the tech sector is where big money and lots of jobs are, but their growth has stagnant in the last year. The group of tech companies that make up the name "FAANG" have been the leaders in this growth, but is that trend still going strong or has the climb become an uphill battle that has met its match ? How is FAANG dealing with a market that is expected to end up in a recession before 2023 meets its end? We'll get to all that, but first things first: what is FAANG? What is FAANG? If I were to ask you to think of the biggest companies out there, which ones would you mention? Probably companies like Google, Amazon or Facebook would be the first few you mention, and without a doubt at least one of the members of FAANG would come to mind. FAANG, so aptly named by Bob Lang back in 2013 and later popularized by Jim Cramer, is a group of the biggest tech companies and refers more specifically to their stocks and status as influential powers in the market. These letters stand for Facebook (currently Meta), Amazon, Apple, Netflix and Google (currently Meta); unsurprisingly, these companies are some of the most powerful giants in the tech world with major influence on what happens in almost everything related to the economy. Let’s take a look at what is going on with them now. FAANG’s Influence Did you say growth?! Unless you have been pulling a Thoreau for the past decade, you have most certainly heard of these five companies without flipping to Bloomberg - and with good reason. For the past ten or so years, they have become staple services that almost everyone uses , and as a result, they have seen consistent substantial increases to their stock prices and unending growth . So much so, they also constitute a portion of the S&P 500 Index, which is used as an indicator of the health of the stock market. This means that since FAANG accounts for a sizable portion of the index, their weight in the system shows that these companies are connected to the overall economy’s health . But sometimes having all this influence can be detrimental . Bubbles have to burst Their impact on the stock market is significant, and some experts fear that this could become a problem in the future due to their potential as a bubble . A bubble in the stock market means that the value of the good has risen above the actual product’s real value and when the bubble bursts , there is a sudden decrease in value . Some examples of a bubble come would be the infamous tulip frenzy of the 1600s , where the prices of tulip bulbs soared until one day a buyer didn’t show up to receive a shipment in a trade-based economy, which caused a major drop in value and a huge panic across Europe. Or another great example would be the US housing crisis of the 2000s . In both cases, when the bubbles of highly influential and overpriced products burst, the prices of those products plummeted as a result. The rest of the market was impacted as well and as a whole, struggled to recover from the shock. Why is this important? Bubbles pop and if you consider FAANG to be one, then it’s possible that they are nearing the point of bursting soon . Unfortunately, this is evident when looking at the 2022 fiscal year. The tech industry has taken a big hit during 2022 ; in particular, Netflix and Meta have dropped by 52% and 65%, respectively. Although other FAANG members like Amazon and Apple have had their fair share of problems with declining prices as well. A bit of a rough patch There are a few contributing factors to why they have hit a rough patch. During the COVID-19 pandemic, tech companies had the advantage given people were at home on their devices, scrolling around and sharing anything they could get their hands on since there was nothing else they could do. This gave FAANG the opportunity to go on a hiring frenzy, but soon enough, confinement ended and people went out into the world leaving their devices unattended at home and corporations had to continue paying for these new employees. To remedy this mistake, layoffs were on the menu . Not only have they been reducing the number of their staff, but also the number of new, open positions has diminished. In addition, since the 2008 housing crisis, interest rates have remained dramatically low , which in turn has made debt quite cheap, and exponential growth became the name of the game for the past few years for the big tech giants. But now that interest rates are increasing thanks to the Fed, the game is changing , and growth and expansion isn’t the best strategy under these circumstances. The smarter move in this situation is expense management and profitability , which means a strategic shift from their years of making growth their principal aim. All in all, the confidence of investors seems to have tapered off too and with a supposed recession on its way, the future does seem a bit bleak for FAANG. What next? Although these big corporations may have to pull back on the reins a bit in terms of their growth, they will still continue to have a big influence on the world’s economy. The decline in hiring opportunities and the potential bubble burst coming does mean trouble for sure. It may seem like FAANG having issues could end their running streak as tech companies, but if they shift their approach of constant growth, they have the potential to thrive again in the future. For now what does that mean for the tech sector? These companies’ growth has started to slow down and taper off, but there will always be a need for more IT professionals ! As the world comes to depend more and more on technology as a basic life necessity, you can be sure that there will still be plenty of jobs available for anyone looking to work as a computer programmer, website designer, or software developer. If that means working with smaller companies or as a freelancer, the number of opportunities is limitless.

  • 5 minutes

    What Does a Career in Web3 Look Like?

    Ironhack - 2022-11-11

    The internet is evolving, and fast. On the web as we currently know it, information is stored on servers, which we can read on our computers. Today, we're moving towards a new type of internet called Web3; where data is decentralized and stored in many different places. From small startups to tech giants, a growing number of companies are investing in Web3, blockchain, and cryptocurrency — and are looking to hire skilled professionals to drive these exciting new technologies forward. But what does a career in Web3 actually entail? And how do you get started? In this blog post, we’ll explore what a career in Web3 really looks like: From what kind of jobs are available, to the skills you need to break into this emerging and innovative field. Let’s dive in! H2: What Kind of Jobs Are There in Web3? Web3 is an emerging space, with the scope for some really exciting and specialized roles in the future. But there are also multiple well-established entry-level Web3 roles; which include tech disciplines like software engineering, web development, community management, data analytics, and design. To give you a sense of what’s out there, we’ve pulled some examples of existing roles from a Web3 job board . Backend engineer @ Kodex UX/UI product designer @ Obol Data Analyst @ P2P.org What Kind of Companies Are Investing in Web3? The next generation of internet technology is happening right now. New, innovative companies are populating the space to create pioneering products and services that will change how we interact with the web. According to 101 Blockchain , the The web3 market could reach a total value of almost $81.5 billion by 2030 — making it a lucrative investment for companies across multiple industries, including finance , healthcare , and government services . Web3 companies are mostly startups, but there are also blockchain projects within larger companies (such as IBM and Twitter ). Most of these companies are working on infrastructure or tools for decentralized systems like Ethereum. Some examples of web3-specific companies include: ConsenSys ; a company offering consulting services, as well as development teams, focused on building applications on Ethereum's platform; and Melonport ; a Swiss startup who aim to create an autonomous community where individuals can manage their digital assets without intermediaries. Among the growing number of companies foraying into Web3 are tech giants like Twitter and Instagram , as well as FinTech companies in the crypto currency space (like PayPal and Alchamy ). Is Web3 Hard to Get Into? As a newcomer to Web3, you’ll be joining thousands of talented professionals seeking to help shape this new technological landscape. But before you do, you might be wondering how hard it is to break into the space. So, is Web3 hard to get into? Well, yes and no. As a new space, there are far fewer ‘guarantees’ than you’ll find in other digital discplines. It’s still unclear what long-term career progression looks like in Web3, as the workforce is technically still being formed. As so many Web3 and blockchain products are still being built, the baseline requirements for entry-level Web3 professionals can vary massively depending on the industry. On the plus side, joining Web3 as one of the early professional cohorts makes the Web3 job market a lot less competitive than other tech disciplines. You’ll be up against fewer candidates for Web3 roles, which means a higher chance of finding a job quickly and more leverage to negotiate a higher salary. Learning Web3 skills also demonstrates a commitment to staking your claim in this rapidly-growing space — which is bound to give you the edge you need when it comes to landing your first role. What Skills Will Web3 Professionals Need? Web3 is a complex and multidiscplinary field which requires a lot of problem-solving and analytics. Entry-level Web3 professionals are expected to be proficient in a wide range of technical skills, including: Front-end development. This includes frontend programming languages like HTML, CSS, and JavaScript — as well as object-oriented programming, DOM manipulation, version control, and ReactJS. Back-end technologies. You’ll need to know how to create a MERN stack application, set up a NoSQL server, and use ExpressJS, NodeJS to create and deploy robust applications. Blockchain and cryptography. Web3 professionals need to be well-versed in the blockchain trilemma and how hash functions, digital signatures and proof of work and proof of stake consensus mechanisms function in practice. Etherium, smart contracts, and token standards. You’ll a working knowledge of smart contract development, Solidity and the Ethereum Virtual Machine (EVM) through writing and testing smart contracts. Decentralized applications (dApp) development and tooling . Understanding how to develop and deploy dApps using front-end libraries such as ethers.js and web3.js. For complete beginners, this list might seem overwhelming — and even intimidating. Worry not! Beginner-friendly courses like IronHack’s soon to be launched Web3 bootcamp will see you learn all the skills needed to forge a career in Web3 — as well as benefitting from tailored career support to help you land your first role. Sign up now to be the first to hear when spaces are available! What Makes a Good Career? Web3 isn’t just a hyped-up buzzword: It’s already a well-established space with limitless potential to transform the way we use the web, forever. With a growing number of big tech companies recognizing the value of Web3, it’s safe to say the field is here to stay — and the demand for forward-thinking Web3 professionals is only set to climb. Web3 might be fertile ground for a new career opportunities, but that doesn’t necessarily mean it’s the right career path for you. High salaries, remote work, interesting projects, a future-proofed career, and other compelling factors might all sound good on paper — but in order to do the job day in day out, you need to feel passionate about the work. In the absence of a crystal ball, you might be wondering how to figure out what career path is right for you. We’ve come up with a resource to help you along your way, with our Career Vision Planner . Check it out!

  • Learn the basics of data analytics: Intro to SQL

    Ironhack - 2021-07-26

    Data is all around us, and it's kind of crazy to imagine what it must have been like to store it all manually in filing cabinets before the digital age came in to make it all easier for us. Luckily, now we have databases (YAY!), but wait, how do we pull the information we need or want from these databases with as little fuss as possible? Drumroll, please… Enter, SQL! But before we dive into SQL and why it's handy... What is Data Analytics? Data analytics is growing more and more in popularity as more businesses move to gathering and storing all their data online, so it's a pretty big deal, especially in the world of business, or governance etc. As you are most probably aware, data is being collected all the time, yet in its raw form, this data will leave you scratching your head because it won't make sense. This is where data analytics comes in: it allows companies to pull out, edit and add specific data they are searching for . This helps such companies or organisations to draw insights and make the most informed decisions for their next strategic move. Data analysts are much sought after because they are able to organise and categorise this data to make it interpretable and therefore usable, and they speak SQL. Btw in case you were wondering, data analysis and data science are two different fields . Data science is more multidisciplinary as it combines statistics, scientific methods, artificial intelligence (AI) and more to extract value from data. Plus they use a range of tools like  smartphones, sensors, websites and more to interpret data. How is data analysis used in the real world? Generally speaking, data analysis can be used infinitely depending on what information is being looked for, yet more specifically, it's used to make better, faster and business decisions to reduce overall business costs and develop new and innovative products and services. For example, it could predict future sales or purchasing behaviours, security purposes as it helps to protect against fraud, analyse the effectiveness of marketing campaigns or increase supply chain efficiency. First steps into SQL: What is it and what is it for? SQL stands for Structured Query Language and is pronounced as Sequel. This is the language used in data analysis to communicate with data ! Three key things to know about SQL Suppose you're in sales, marketing, business etc. SQL is probably the one coding language you should learn as most companies have an online presence and are collecting data. So the more you know how to communicate this data, the better you can pull and analyse and the better you are at your job! If this is you, you can pop into our webinar to learn the basics of Data Analytics ! SQL languages have syntax variations . Different companies follow different databases sets, they are only slight variations, but it is essential to be aware of them. SQL only communicates with relational databases . So any database with a tabular organisation (with rows and columns). This leads us to our next point. SQL and RDBMS RDBMS stands for Relational Database Management System. This drastically helps to organise data so that it can easily be extracted and analysed. Let us break it down even further so you really know what it means. It's a system that manages data organised in tables and the relations between them. Let's break it down again. When we talk about systems we mean RDBMS can store many different kinds of data for many different kinds of applications in one place .  For example, if we are talking about a sale, there is a table or dataset for the sale information, one for customer information and the other for the sales item or inventory; therefore, there is a relation between these data sets. Key benefits of RDBMS: System : can store many styles of data for multiple applications Manager : stores, indexes, keep safes, backups Data : all data can be stored but mostly numbers and strings Tables : organised in columns and rows Relationation : patterns between different values in columns and tables are linked together. So, why RDBMS? Because it’s an effective and reliable way to store information as the basis for online transaction processing systems, and these are systems that keep businesses running. RDBMS are applied in corporate administration and accounting, banking and insurance systems, government data, point of service (POS) and E-commerce systems, and the list goes on. And of course, all the data gathered in these systems are used in the analytics environment to generate insights, but you first need to access such data. And how do we do that?...Through SQL, which speaks to RDBMS! Some key terms in SQL A SQL query allows you to query (investigate) a specific piece of information. Tables Tables are the database objects that hold the data in the relational databases . SQL can be applied to programs like Python or even a simple excel spreadsheet. In SQL lingo, a Column is a field, and a Row is a record, and finally, there is an entity that is the smallest unit that contains a meaningful set of data. An entity is also known as the dataset object . With SQL, the best way to learn is by giving it a go! Yet, here are some quick terms to get you familiar with the lingo. Select, from and where If you are looking to query something, the term "select" always comes first and is eventually followed by "from" . The "where" term allows you to filter out rows that you do now want to have in your search results. Here is a quick example of what it could look like if we were to be using a student database: Select: “*’” or the specific field eg. (student_name_dateofbirth) From: (table name) e.g Student Where: date of birth = (select max ( date of birth) from student) By the way, aside from the equal "=" sign you can use does not equal to "<>" or is greater than "<" ; the list goes on, and you change this based on what you are looking for. When you want to merge two or more tables or datasets, you can use the "join" statement that puts two different tables together side by side based on a shared value; the term joins usually appears after from but before the where statement. For example; From table_1 join table_2 Union Finally, we will mention one more! The “union” term matches columns from top to bottom . This union statement usually sits between two select statements . The union can only occur on columns with the exact same column names and columns that have the same data type. To continue learning more about the basics of Data Analytics and SQL , watch our webinar below: powered by Crowdcast All in all, getting into data analytics can be super interesting and satisfying once you get the hang of it, it's like going on treasure hunts! And if you’re really interested in becoming a serious data analyst pro, want to build your career or seriously widen your job prospects look no further than our insightful Data Analytics Bootcamp!

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