A data analyst sitting at a desk, working on a laptop

How to Become a Data Analyst

Emily Stevens

So you want to become a data analyst. You’ve done lots of research and decided it’s the career for you—or perhaps you keep hearing about this rather interesting job title and are curious to learn more. Either way, you want to know exactly what a career as a data analyst entails and, most importantly, how you can make it happen.

Well, you’ve come to the right place. You can think of this article as your complete data analyst career guide. Not only will we set out the specific steps you need to take to become a data analyst; we’ll also give you a good overview of what a data analyst actually does, and show you the most important hard and soft skills you’ll need in order to be successful (and employable) in the field. Follow the advice in this guide and you’ll be well on your way to becoming a fully-fledged data analyst.

In this article, you’ll find the following sections:

  1. What does a data analyst do?
  2. What is the typical background of a data analyst? 
  3. What skills do you need to become a data analyst?
  4. How to become a data analyst: Your 5-step plan
  5. Key takeaways and next steps

If you’re keen to get straight to the action, simply use the clickable menu above to navigate to the step-by-step guide in section four.

So, how can you go from complete beginner to employed data analyst? Let’s find out.

1. What does a data analyst do?

First things first: What does a data analyst actually do? 

If you’re looking to carve out a career in the field, it’s important to know what the work of a data analyst entails—and how it differs from other roles. In simple terms, data analytics is the process of analyzing raw data in order to draw out meaningful, actionable insights. These insights are then used to help businesses make smart decisions.

A data analyst seeks to answer specific questions or challenges that are relevant to the business, such as “How can we boost customer retention rates?” or “How can employee satisfaction be improved?”. To do this, they collect or extract raw data from relevant sources (a CRM database, for example, or an employee satisfaction survey), organize and clean their dataset (for example, by removing duplicates), and analyze it using the appropriate technique. The type of analysis performed depends on the questions being asked and the type of data involved, but the analyst will usually be looking for patterns and trends. With the analysis done, they visualize their findings in the form of graphs and charts before presenting them to stakeholders. Finally, based on what the data tells them, the analyst will make recommendations about what the company’s next steps should be.

Data analysts work with a range of business intelligence and analytics tools. They are typically expected to be proficient in software like Excel and, in some cases, querying and programming languages like SQL, R, SAS, and Python. To work as a data analyst, it’s important that you’re comfortable using such tools and languages to carry out data mining, statistical analysis, database management, and reporting—but we’ll take a closer look at the skills and tools you need to master in section three.

If you’re thinking about becoming a data analyst, it’s also important to understand how the role differs from other job titles in the field. When researching the data analyst role, you’ve probably come across a lot of content which talks about data science. Despite the fact that these two terms are often used interchangeably, they are two separate career paths which serve different purposes—and require very different skills. If you’re unclear on how the two roles differ, have a read through this guide which explains the main differences between a data scientist and a data analyst. And, if you’d like to learn more about what it’s like to work as a data analyst, this interview with data analyst Radi will provide some first-hand insight.

2. What is the typical background of a data analyst?

Now we know, broadly speaking, what a data analyst does, you might be wondering: What is the typical background of a data analyst? What experience do I need?

As we’ve seen, a career as a data analyst will see you bridging the gap between data and business strategy. Data analysts are, therefore, very comfortable working with numbers. They also tend to bring at least some business acumen to the table. In terms of formal education, data analysts typically study related subjects, such as:

  • Maths and / or statistics
  • Finance and / or economics
  • Computer science
  • Information management
  • Business information systems

However, that’s not to say that you can’t become a data analyst if you don’t have a degree in one of the above subjects. There are many other fields of study or professional experience that can prepare you for a career in analytics, including marketing, IT, and customer service—to name just a few. In fact, any role that sees you applying problem-solving skills, working with some form of data, or seeing the inner workings of a business will give you a good foundation upon which to build your career as a data analyst. Yes, some positions will require a degree in a specific field, but there are also plenty of opportunities out there for newly trained analysts who haven’t necessarily come from a typical data background.

Learn more: Am I a good fit for a career as a data analyst?

So: If you want to become a data analyst, the only real prerequisites are an affinity for numbers, an interest in business, and a good degree of intrinsic motivation—the rest can be learned. Whether you’ve already got a relevant degree or are coming from a completely unrelated field, it is entirely possible (and realistic) to train as a data analyst. The most important thing is to master, and demonstrate, the right skills for the job. Which brings us on to section three…

3. What skills do you need in order to become a data analyst?

Now we’re getting to the crux of what it really takes to become a data analyst. In this section, we’ll outline some of the key hard and soft skills that employers are looking for when hiring data analysts. We’ve also included some tools and languages that data analysts might work with. Not all of these skills and tools are essential for every data analyst role, but we’ve included those that frequently come up in real job descriptions.

Data analyst soft skills

  • Communication, collaboration, and presentation skills
  • Problem-solving
  • Research
  • Attention to detail
  • An analytical mindset
  • An affinity for numbers
  • Good organizational skills and an ability to meet deadlines
  • Some commercial knowledge or business acumen
  • A methodical and logical approach

Data analyst hard skills and tools

  • Proficiency in Microsoft Excel
  • Knowledge of programming and querying languages such as SQL, Oracle, and Python
  • Proficiency in business intelligence and analytics software, such as Tableau, SAS, and RapidMiner
  • The ability to mine, analyze, model, and interpret data
  • The ability to work with large, complex datasets
  • Solid understanding of data profiling and requirement gathering processes and principles
  • Expertise in data visualization
  • The ability to communicate findings and to make actionable recommendations for the business
  • The ability to deploy commercially viable statistical models 

It’s important to note that data analysts can work in pretty much any sector—from finance to healthcare to marketing and beyond. Most organizations gather raw data and hire analysts to turn it into actionable insights. This means that, in addition to the core skills outlined above, each company will come with its own unique set of requirements. You’ll get a good idea of the kinds of companies hiring data analysts and what they’re looking for simply by browsing through job ads. We recommend searching “data analyst” on sites like Indeed and LinkedIn. And, for a more in-depth look at the skills you’ll need as a data analyst (and why they’re so important), have a read through this guide: What are the key skills every data analyst needs?

4. How to become a data analyst: Your 5-step plan 

By now, we’ve got a good understanding of the kinds of tasks and requirements that come with the data analyst job title. If you like what you’ve read so far, it’s time to think about how you can make it a reality. In this section, we’re going to answer that all-important question: How can I become a data analyst? 

We know that career-change can be a daunting prospect, so we’ve broken it down as simply as possible. In just five steps, you can go from aspiring data analyst to fully-fledged professional. Here’s how.

Step 1: Get familiar with the fundamentals

This first step is all about immersing yourself in the world of data analytics and getting familiar with some of the key tools and principles. There are two components to this step:

  1. Embracing the theory behind data analytics
  2. Getting hands-on with some key data analytics tools

Before you do anything else, absorb as much theory as you can about data analytics. Read about how data analytics is being used in the real world, and familiarize yourself with the kinds of analyses that a data analyst might perform. As a focal point for your reading, we recommend researching the different types of data analysis—descriptive, diagnostic, predictive, and prescriptive—and getting to grips with the various data analytics techniques, such as regression analysis, factor analysis, cohort analysis, cluster analysis, and time-series analysis. This will give you a good theoretical foundation upon which to build those all-important practical skills. For a comprehensive and accessible entry point, start with our ultimate introduction to data analytics.

With some good theory behind you (or at the same time), you can start to explore some key data analytics tools. For any aspiring analyst, the best place to start is Microsoft Excel. Download an open source dataset (you’ll find a list of ten great websites for finding free, open datasets here) or devise your own and practice carrying out some basic tasks, such as creating a pivot table or practicing some useful formulas. We’ve included some helpful links below to get you started:

Remember: The goal isn’t to teach yourself everything. At this stage, you’re just looking to get the lay of the land. Once you’ve gained some first-hand insight into what data analysts do and the kinds of tools you’ll be working with, you’re ready to move on to step two.

Step 2: Commit to the process with a structured course

Now it’s time to get serious about your career-change. While the internet is full of wonderful and free resources, it won’t give you the structured approach or the hands-on practice that you need. If you want to become an employable data analyst, the most effective (and rewarding) way to do so is through a specialized program or course.

The good news is, there are lots of courses to choose from. With the rising demand for skilled data analysts, we’ve seen a surge in courses and bootcamps that promise to take you from data newbie to job-ready professional. However, not all data analytics courses are created equal, so this step requires extensive research. When choosing a program, it’s crucial to find a structure that complements your schedule (especially if you plan on working and studying at the same time) and fits your budget. Most importantly, however, we strongly recommend investing in a course that can offer the following:

  • Mentorship: The best courses on the market offer one-to-one mentorship, which is crucial for staying motivated and getting genuine feedback on your work.
  • A practical, hands-on curriculum: Many courses will teach you the theory with the help of curated resources, but what’s really important is that you master and apply the necessary practical skills as you go. Look for a course that will test your knowledge continuously with practical exercises, as well as focusing on project work and helping you build your portfolio.
  • Career coaching and a job guarantee: If your primary goal is to get hired as a data analyst, it’s important that you have access to expert career advice. Some of the top-tier courses on the market offer career coaching and a job guarantee, ensuring you find the right role once you’ve finished your studies. 

Now, we realize that finding the right course can feel like searching for a needle in a haystack. To guide you in your research, we’ve compiled a thorough market comparison: you’ll find a list of what we consider to be the best data analytics bootcamps on the market here. 

Step 3: Hone your soft skills 

In addition to your chosen data analytics course, there’s plenty you can do to enhance your learning experience. While the course takes care of those all-important hard skills, it’s a good idea to start honing your soft skills.

If you need a quick refresher on the most important soft skills a data analyst should demonstrate, refer back to our list in section three. Armed with this list, think about the soft skills you’ve already perfected and identify those that might need more work. Perhaps you’re excellent at conducting research and solving problems as part of your current role, but haven’t had much experience of giving presentations. If there’s opportunity for this kind of development within your current workplace, grab it with both hands. Offer to run a small research project and present your findings to the team, or put your analytical mindset to work by offering to tackle a specific business problem.

This step does require some creativity, especially if your current role doesn’t immediately lend itself to such opportunities. If you don’t feel that you’re able to develop certain soft skills in your current job, seek opportunities elsewhere. Informal meetups are an excellent way to practice your presentation skills, for example. Although it may seem small, this step in the process is a crucial part of your journey to becoming a data analyst. When it comes to applying for jobs, you want to be able to list as many of the necessary skills on your CV as possible—and, oftentimes, the right soft skills will give you a major advantage.

Step 4: Start networking

It’s the golden rule for every industry: establishing a good network and making connections is absolutely essential if you want to get your foot in the door. While many of us dread the idea of “networking”, it doesn’t have to be as uncomfortable as it might sound. In fact, there are many different types of networking, ranging from a friendly message on LinkedIn to a full-on conference circuit.

As an aspiring data analyst, it’s important to start connecting with like-minded people as early as possible. Not only does a good network open up potential doors in terms of career prospects; it’s also an excellent source of mentorship and support as you find your feet in a brand new industry. So where to start? Here are some excellent ways to network, both online and in person:

  • LinkedIn—join some data analytics groups, or simply connect with analysts in and around your local area.
  • Meetup.com—Meetup offers loads of tech and data meetups all over the world, ranging from the free and informal to the more professional events.
  • Your student community—many data analytics courses will offer you ways to get in touch with the wider student community, through Slack for example. This is a great way to lend and receive peer-to-peer support and potentially make offline connections, too. 

If you’re not a natural-born networker, it might be tempting to skip this step. However, it’s important to surround yourself with people who you can learn from and share experiences with—not to mention the possibility of a good connection leading to a future job opportunity. If you’d like some tips, check out this guide on how to network like a pro.

Step 5: Refine your portfolio and prepare for the job market

The fifth and final step towards becoming a data analyst is to refine your portfolio and prepare for the job market.

Your portfolio is arguably the most important asset you’ll have when it comes to applying for jobs; it showcases how you work and demonstrates to employers that you’ve not only mastered the right practical skills, but that you know how to apply them in the real world. As already mentioned, the right data analytics course will have you working on practical projects and guide you in the creation of a professional portfolio. Your mentor can also help you to polish up your portfolio and give you tips on how to present it. You can learn how to create a data analytics portfolio in this guide. And, if you’re not sure what to include in your portfolio, check out these nine data analytics project ideas.

Aside from your portfolio, you’ll want to make sure that your online presence is optimized for your new career as a data analyst. This includes updating your LinkedIn profile to highlight all the relevant skills and tools you’ve mastered, and including a summary of the kind of role you’re looking for. Again, a good data analytics course will help you optimize your online presence and devise an effective job-search strategy.

Once you start applying for jobs, it’s a good idea to prepare for the interview process. Although every company will have their own interviewing techniques, there are some common questions you can expect to be asked when interviewing for a data analyst position. To help you prepare, we’ve put together some of the most common data analytics interview questions (and how to answer them) in this guide.

5. Key takeaways and next steps

That just about concludes our guide on how to become a data analyst. You now have a clear, step-by-step plan to follow in order to learn the necessary skills and break into the industry. Before we go, let’s recap on your plan of action:

  1. Get familiar with the fundamentals: Learn about the different types of analysis and practice using some formulas in Excel.
  2. Sign up for a data analytics course: Opt for a certification program that offers expert mentorship, career coaching, help building your portfolio, and a job guarantee. 
  3. Hone your soft skills: Refer back to our list in section three and make sure you can demonstrate all of the soft skills needed to become a data analyst.
  4. Start networking: Join data analytics groups on LinkedIn, get involved in your student community, and consider attending local meetups.
  5. Refine your portfolio and prepare for the job market: Work with your mentor and your careers coach to get your professional portfolio ready, optimize your online presence, and start practicing for interviews.

If you’re keen to dip your toes in, you can try a free introductory data analytics short course here. In need of some further reading? Check out the following:

What You Should Do Now

  1. Get a hands-on introduction to data analytics with a free, 5-day data analytics short course.
  2. Take a deeper dive into the world of data analytics with our Intro to Data Analytics Course.
  3. Talk to a Career Advisor to discuss career change and find out if data analytics is right for you.
  4. Learn about our graduates, see their portfolio projects, and find out where they’re at now.

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Emily Stevens

Emily Stevens

Managing Editor at CareerFoundry

Originally from England, Emily moved to Berlin after studying French and German at university. She has spent the last five years working in tech startups, immersed in the world of UX and design thinking. In addition to writing for the CareerFoundry blog, Emily has been a regular contributor to several industry-leading design publications, including the InVision blog, UX Planet, and Adobe XD Ideas.