Data analyst is undoubtedly one of the hottest new jobs of the 21st century. But what qualifications do you need to become one? Let’s find out.
Thanks to surging demand, there’s been a wave of newly qualified data analysts moving into the field in recent years. This isn’t too surprising: data analysts are one of the most sought-after hires in companies around the globe. They’re so needed, in fact, that demand exceeds supply. As challenging as this is for businesses, it’s great news for job seekers looking to move into a fulfilling and financially rewarding profession. But this begs the question: What qualifications do you need to become a data analyst?
In this post, we explore the formal qualifications that people often focus on when building a career in data analytics, as well as the relevant experience that can qualify you for a career in this creative and exciting field. We’ll ask:
- What does a data analyst do?
- Do I have the skills to become a data analyst?
- Do I need a specific degree to become a data analyst?
- What topics will a good data analytics qualification cover?
- How do I get started in data analytics?
- Wrap-up and further reading
Before we find out what qualifications you need to become a data analyst, let’s quickly ask:
1. What does a data analyst do?
In a nutshell, a data analyst’s job is to obtain insights from raw data, usually to help inform decision-making in business. While the overall data analytics process entails a great number of technical skills and tasks, it ultimately involves collecting, organizing, and exploring data to identify patterns and find meaning in those patterns.
Usually, a data analyst will translate their findings into one or more actionable recommendations. How these recommendations look will depend on the nature of the problem and the job itself. In business (where the majority of analysts are sought) data analytics is commonly used to support things like new product development, to drive sales strategies, to boost supply chain efficiency, or to evaluate the effectiveness of marketing campaigns. Data analytics is used across many industries, too, from finance to education and healthcare, making this a great career in terms of job security.
Rather than diving into more detail here, you can learn more about what a data analyst actually does in this post. Needless to say, all the skills that data analytics calls upon have to be learned, which is where a qualification comes in.
2. Do I have the skills to become a data analyst?
If you’re considering a career change and data analytics has caught your eye, there are three main skill types to consider. These are:
- Technical knowledge
- Transferable and soft skills
- Industry-specific know-how
Let’s look at these more closely.
First things first: all data analysts require certain technical skills. This includes things like programming, understanding different analytical models (and when to apply them), and other theory and tools around the data analytics process. Do a quick online search and you’ll find that technical know-how makes up the bulk of discussion around data analytics jobs.
This can be quite intimidating if you’re not familiar with these types of skills, or the industry jargon that goes with them. But don’t be put off! Focusing on technical skills alone does data analytics a bit of disservice. Yes, these skills are necessary; but focusing only on technical know-how overlooks how creative and varied the field can be. Technical knowledge is important, but it’s all stuff you can learn. We’ll cover this more in section four.
Transferable soft skills
In the initial stages, perhaps more important than your technical knowledge is whether or not you have the right transferable skills. Increasingly, employers say that they want soft skills, something that traditional qualifications (like college degrees or accredited courses) don’t usually develop or measure.
Soft skills include things like communication, teamwork, a positive attitude, entrepreneurship, and a strong work ethic. As such, if you’re figuring out whether a career as a data analyst might be right for you, don’t worry about the technical stuff just yet. Start by asking yourself: what transferable skills do you have? Are you creative? A critical thinker? A problem-solver? If so, you likely have the core traits required to thrive in data analytics. This guide will help you figure out if you’re naturally a good fit for a career in data analytics.
Data analytics is used in an increasingly wide range of industries, from retail to healthcare, government and energy, to name a few. As such, any industry-specific knowledge you might have will help you stand out to employers. For example, perhaps you spent several years working for an insurance firm, or maybe you understand how supply chains work because you spent a summer working at a department store—all this is valuable knowledge.
None of this is to say you need industry-specific knowledge: like technical know-how, it’s possible to pick it up. But research shows that companies increasingly want business people with analytical skills, rather than analysts who are experts from the word go but may not have domain knowledge. As you carve your path in the field of data analytics, it’s worth taking into account what you already know and how you can leverage this to your advantage. Now, to the crux of our post…
3. Do I need a specific degree to become a data analyst?
Let’s assume you have a creative streak, you’re enthusiastic to learn, and you’re willing to seek out the opportunities necessary to hone your craft. What about the technical knowledge? Do you need a specific degree or qualification?
While an undergraduate degree, Master’s, or even Ph.D. in a field like math, statistics, or computer science will certainly stand you in good stead, none of these is the prerequisite to a career in data analytics. A certification of your knowledge is often all you need (and even then, not always, as we’ll see).
Why do people think a formal degree is so important?
There’s a common misconception that a formal degree is necessary if you want to land a job as a data analyst. This misleading view stems from a common confusion between data analytics and data science—two related but distinct fields, which are often described interchangeably.
Often, data science roles (which incorporate data analytics alongside a range of other specialist skills) require a formal undergraduate or postgraduate degree. Because data science is often confused with data analytics, people sometimes believe they also need a degree for the latter. In fact, for entry-level data analytics roles, a suitable certification program paired with the right attitude is usually more than sufficient. Providing you have the basic skills and knowledge, employers are often happy for you to learn on the job.
Can I do without a qualification altogether?
In the past, people often had to undertake a formal qualification to do their job and then they would stay in that job for life. But in the 21st century, as industries and skills quickly evolve to meet the needs of a fast-changing world, this is an increasingly outdated concept. With new fields like data analytics still emerging, there’s no longer a prescriptive path you have to follow. So, is it possible to do without a qualification altogether?
Possibly, although don’t count on it. If you already have technical skills under your belt, you may not need a certification. For example, software developers often naturally progress into data analytics because the two roles share similar skills (i.e. programming). If it’s the case that you already have a broad toolset of technical knowledge and skills, you might be able to land your first data analytics job without any qualification. In the longer term, however, it may be necessary to take a certified course to cement your knowledge.
However, this route is only really likely if you’ve already spent years working in areas like software development or statistics. If you don’t have those technical skills yet, a certified course remains highly advisable.
What does the job ad say?
If all else fails, be sure to check the language used in job ads. Sometimes they’ll ask for a formal qualification, but they aren’t always specific. Do they want a particular degree, or will a shorter certified course do? Employers increasingly understand that nurturing employees is the best way to keep them in their role. As a result, many now offer formal study or continuing professional development as part of the role. In short, there are many routes to success, so don’t worry too much about whether you have a degree or not.
4. What topics will a good data analytics qualification cover?
Presuming you’re not going down the degree route, which can take many years, what should you look for in a certified course? While the answer may vary if you’re exploring specialized options (for instance, data analytics in a particular industry), common themes will include:
- Preparing and analyzing data
- Data mining
- Exploratory data analysis
- Data visualization and dashboards
- Database management
- Programming skills
Since every qualification will interpret the technical aspects slightly differently (and teach them in varying depth) we won’t get into detailed specifics here. Rather, we’ll focus on the broad areas you should look for in any effective data analytics program, whether that’s a college degree, data bootcamp, or online tutorial. If you already have your heart set on taking a course, check out this guide to the best data analytics certification programs.
Preparing and analyzing data
First, you’ll need to understand which data analytics skills you need to solve a particular industry or business problem. This will involve learning how to ask the right questions to identify a problem, what data you need to solve it, and how to collect these data. From a practical standpoint, this commonly involves learning how to use basic data manipulation tools like MS Excel.
Collecting, or mining, data is a key aspect of any analyst’s job. You’ll not only need to learn technical approaches to data mining (such as how to program a web scraper) but also the ethical and privacy issues relating to data collection.
Exploratory data analysis (EDA)
EDA is one of the early steps in the data analytics process. It’s used to help you get to grips with the data you’re working on, as well as hypothesizing solutions to problems. A good data analytics course should therefore teach you the theory behind EDA, as well as the tools and skills you’ll need to carry one out.
Data visualization and dashboards
Visualizing data is key, both for exploring it and for sharing your insights with others (e.g. via interactive dashboards). A good data analytics certification will therefore introduce you to different kinds of data visualization and how to use them effectively in different contexts. As a starter, you can read a summary of the most common types of data visualization in this post.
Creating, managing, and pulling information from databases is an obvious and fundamental aspect of data analytics. At a bare minimum, you’ll need to understand the fundamentals of database management, including how to use SQL (structured query language), an industry-standard language for communicating with relational databases.
Creating and tweaking algorithms is another vital part of data analytics. Any course worth its salt will teach you basic programming skills. This will usually be in Python. However, more specialized courses might also include languages like R (which is popular for statistical programming and is commonly used in the sciences).
While these topics aren’t things you can learn overnight, they are eminently achievable. It’s quite possible to pick up the basics in a matter of months, or even weeks, rather than the years that it would take to complete a full college degree. So, be sure to keep an eye out for all these topics in any course you’re considering.
5. How to get started in data analytics
As we’ve seen, data analytics is creative, varied, and offers great job security. If you’re convinced that this is the right path for you, there are plenty of ways to get started.
To begin with, make sure you read up on the field. There’s a lot to get to grips with, from big picture stuff, like learning about the importance of big data, to picking up task-specific skills, like how to calculate variance using MS Excel. You can also check out sites like Twitter and Medium, where data analyst gurus share the latest thinking on data-related topics.
While you might not be ready to create your own just yet, it’s also an idea to start looking at different data analyst portfolios. Use this as inspiration for your future portfolio (as well as to see what the competition is up to!)
Finally, you’ll need to get started with learning all those skills outlined in section four. If you’re not ready to commit to an intensive program, why not start with a free data analytics short course, or look into what kinds of data analytics bootcamps are available?
If you’re ready to dive in, there are many certified programs available to choose from. CareerFoundry’s own Data Analytics Program has been created with beginners in mind, taking you from novice to professional data analyst within eight months. Alongside mentoring, the course also offers a job guarantee, minimizing your risk if you’re stepping out of your old career into a new one.
6. Wrap-up and further reading
In this post, we’ve explored the types of qualifications you might need to become a data analyst. Hopefully, you should now have a better idea of whether a data analyst career is right for you. We’ve determined that (initially, at least) an interest in the topic and eagerness to learn is more important than a technical qualification.
We’ve also established that, despite common misconception, you don’t need a college degree to land a data analytics job. A certified course of study is plenty to prove that you have the relevant technical skills. From here…the sky’s the limit. Good luck!
Want to learn more? Check out the following: