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 roadmap.
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 data analyst roadmap and you’ll be well on your way to becoming a fully-fledged data analyst—and feel free to check out this free short course for a very hands-on introduction to data analytics.
In this article, you’ll find the following sections:
- What is the state of the data analytics industry in 2024?
- What does a data analyst do?
- What is the typical background of a data analyst?
- What skills do you need to become a data analyst?
- Data analyst roadmap: your 5-step plan
- Key takeaways and next steps
So, how can you go from complete beginner to employed data analyst? Let’s find out.
1. What is the state of the data analytics industry in 2024?
If you’re thinking about becoming a data analyst, you’ll want to know: Is this a smart career move? In the wake of the AI hype of 2023, what can you expect from the year ahead? Let’s take a look.
Are data analysts in demand in 2024?
When considering a career as a data analyst, it’s important to think about the wider context in which you’ll be working.
As individuals, we’re generating masses of data all the time—data that is interesting for businesses and organizations as it tells them something about how we behave in relation to their products or services. The more we rely on digital devices and services, the more data we generate—and, in turn, the more important it becomes for companies to make sense of this data.
The data skills shortage
To give you an idea of just how big the big data market is, it’s estimated that by 2025, it’ll be worth $229.4 billion. What does that mean for you? Well, the data market is growing exponentially, and so is the need for data analysts.
This sentiment is echoed in the World Economic Forum’s Jobs of Tomorrow report (published in 2020), which identifies data and AI is one of seven high-growth emerging professions. Of all seven professions identified, data and AI shows the highest growth rate at 41% per year. As it currently stands, there aren’t enough data experts to meet this need; according to a recent study, employers expect data science and analytics to be one of the most challenging areas to recruit for—second only to cybersecurity.
The rise of big data
All of the most effective and successful products, services, and strategies these days are data-driven—from our understanding of the COVID-19 pandemic to those spot-on recommendations we get from the likes of Netflix and Spotify. Data is everywhere, being generated in huge volumes at a rapid pace. Wherever there is data, there is a need for data analysts.
The advent of the age of AI
Enter the generative AI explosion of last year, as large language models (LLMs) led by Open AI’s ChatGPT, promised to revolutionise things. Not just the tech industry itself, but the possible applications of these spread far and wide, and demand grew immediately for professionals who were equipped to understand them. This truly was the year of the machine learning engineer, a data pro skilled at planning and manipulating LLMs and harnessing the power of generative AI for businesses. Suddenly there are over 15,000 open ML engineer positions on Indeed.com.
So, to answer the question: Data analysts are very much in demand in 2024, and will continue to be for the foreseeable future. Great news for anyone considering a career change!
How does remote work affect data analyst jobs?
It’s impossible to talk about the data industry in 2024 without considering the previous few years. The pandemic has changed many aspects of how we live and work, and the data industry is no exception.
One of the biggest and most obvious shifts is the rise of remote work, which increased by 300% since the pandemic. As a data analyst in 2024, you should anticipate working remotely at least some of the time—if not on a full-time basis as the pandemic continues. If you’re learning data analytics through an online program, you’ll already have an advantage when it comes to showing employers that you can work autonomously and remotely.
We’ve covered the topic of what it’s like to work as a remote data analyst in this guide, including an overview of the remote job market and tips on where to look for remote data analyst jobs.
How can you stand out as a newly qualified analyst?
As we’ve seen, data analytics is a rapidly growing field, and data analysts are in high demand. Still, breaking into a new industry can be daunting—especially in such unpredictable times as these. You’ll need a few strategies up your sleeve in order to find relevant opportunities and set yourself apart.
We recently spoke to Mike McCulloch, Head of Career Services at CareerFoundry, about what it’s like for new graduates navigating the current job market, and what they can do to increase their chances of success. We recommend checking out Mike’s advice in full in our guide to job searching, but here are some tips at a glance:
- Target high-growth sectors that have survived and / or thrived during the pandemic; for example, healthcare and health services, home delivery and logistics companies, online education and remote learning, and digital media and entertainment companies.
- Focus on personal branding. As a newcomer to the field, it’s important to market yourself in a way that highlights both your newfound skills and any transferable ones you bring from your previous career. You can learn how to start building your personal brand here.
- Use your “newcomer” status to your advantage. Although competition for jobs might be especially fierce right now, you offer many unique perspectives and benefits as a newcomer to the field. Be sure to highlight these in your applications. This is something a career coach can help you with, so choose a program that offers such support. You’ll find a comparison of some of the best data analytics certification programs here.
How much can data analysts expect to earn in 2024?
Last but not least, how much can you expect to earn? Data analysts can expect to earn an average salary of $76,000 USD. Senior data analysts can currently expect to earn around $96,000 USD.
While this salary data provides a good benchmark, it’s important to bear in mind that salaries vary depending on location, how many years of experience you have, and the industry you work in. We take a closer look at data analyst salaries in this guide. And, if you’re interested in which industries pay the highest data analyst salaries, check out this post.
Without further ado, let’s look at how you can make your career-change happen, starting with a recap of what data analysts actually do in their day-to-day work.
2. What does a data analyst 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.
Tools
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 such as:
- SQL
- R
- SAS
- 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.
Job titles
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.
3. 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 possess one of these degrees. 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.
Take Chad, for example. He studied history at university, then ended up working as a recruiter for years. This didn’t stop him a all and, after completing the CareerFoundry Data Analytics Program, he became a data analyst at the British newspaper The Telegraph!
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?
When it comes to learning how 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. We look in more detail at whether it’s possible to land a job as a data analyst with no prior experience in this post. We’ve also looked at some of the top data analytics schools on the market, if you’re interested in going down that path.
The most important thing is to master, and demonstrate, the right skills for the job.
4. What skills do you need in order to become a data analyst?
Now we’re getting to the crux of how 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 roadmap: 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 roadmap: 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.
5. Data analyst roadmap: 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?
- Get familiar with the fundamentals
- Commit to the process with a structured course
- Hone your soft skills
- Start networking
- Refine your portfolio and prepare for the job market
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. For now, here’s your step-by-step plan of action!
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:
- Embracing the theory behind data analytics
- 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 and getting to grips with the various data analytics techniques, such as:
- regression analysis
- factor analysis
- cohort analysis
- cluster analysis
- 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 in our guide) 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:
- 10 Excel formulas every data analyst should know
- How to create a pivot table: A step-by-step tutorial
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 on the roadmap 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.
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 or data analyst internships, 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.
It’s also worth getting familiar with some common data buzzwords.
6. Data analyst roadmap: next steps
That just about concludes our data analyst roadmap—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:
- Get familiar with the fundamentals: Learn about the different types of analysis and practice using some formulas in Excel.
- 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.
- 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.
- Start networking: Join data analytics groups on LinkedIn, get involved in your student community, and consider attending local meetups.
- 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.
In need of some further reading? Check out the following: