If you’re considering a career change and are hungry for greater flexibility in a well-paid profession, you may be considering becoming a data analyst. With the right skills and an enthusiasm for new challenges, data analytics can be a fascinating, varied, and rewarding role. Demand for data analysts has only been increasing over the years, too, according to the World Economic Forum’s Future of Jobs report.
But hold up a moment: data analytics is pretty technical, right? Surely you need a degree to get started? Having a formal degree certainly won’t hurt your chances, but nor is it the prerequisite it once was. Many employers—especially startups, who tend to be more flexible—understand that a piece of paper is not the be-all and end-all of one’s career. Increasingly, they are more interested in candidates with the appropriate knowledge, skills, and competencies, than they are in what piece of paper you hold.
In this post, we explore the types of skills and qualifications you might need to land your first job as a data analyst. We’ll cover:
- What do data analysts do?
- What skills do you need to become a data analyst?
- Do you need a degree to get a job as a data analyst?
- The best data analytics training options
- Wrap-up and further reading
Ready for the low-down on data analytics and degrees? Let’s jump in.
1. What do data analysts do?
Before we get into what data analysts do, let us cover the basics. If you already know what a data analyst does, you can skip straight to section 2.
What is data analytics?
Data analytics involves analyzing raw data to spot patterns and trends. These are then used to inform decision-making. Essentially, data analytics is a form of business intelligence, and it is used in areas as diverse as marketing, finance, healthcare, insurance, education, transport and logistics, and more. There’s much more to it than this, of course, so be sure to check out our complete data analytics guide for beginners.
What does a data analyst do?
A data analyst’s day-to-day tasks vary a lot, depending on things like the industry, the area of business (for example, marketing vs. product development), and even which aspects of data analytics are most important to the company (for example, data insights vs. data engineering). However, there are some general tasks that all data analysts will have to carry out at some point or another. These include:
- Devising a driving question: A data analyst’s first task is to identify an objective: what problem are you trying to solve? Perhaps you work for an insurer and want to understand why particular cohorts of customers keep making a claim. Or maybe you’re a product analyst seeking to grasp why users have turned away from a previously popular product. You’ll need to frame your problem as a query that you can answer with the appropriate data. This is your driving question.
- Collecting data: Identifying suitable data sources is vital for answering your driving question. Insurers might collect customer credit data, for example. There are many sources of data, though. You might scrape it from the web, collect it through surveys or questionnaires, customer reviews, or from finance and sales figures (to name a few). To get the bigger picture, data analysts will usually collect varied data types.
- Cleaning and storing data: Alone, raw data is not of much use. Before identifying patterns and trends, data analysts must first clean datasets and appropriately store them in a database system. Data cleaning is an involved process on its own and involves removing errors, duplicates, outliers, and unwanted data points.
If you’re interested in how data cleaning works, you may enjoy this video tutorial we worked on with Dr. Humera Noor Minhas:
- Analyzing and interpreting data: Data analysis is an iterative process. Initially, data analysts conduct exploratory analyses to devise a hypothesis (a possible answer to their driving question). Later, they’ll dive deeper to extract detailed insights. Different types of data analysis vary in depth and complexity.
- Sharing results: Once data analysis is complete, analysts must present their findings. Insights are commonly shared through written reports, visualizations, and presentations. They must always be accessible to the target audience.
That sums up a data analyst’s job in a nutshell—but to learn more, check out this guide explaining what a data analyst does.
2. What skills do you need to become a data analyst?
Now that you have a clearer idea of what data analysts do, you may find yourself asking: what skills do data analysts need? Broadly, data analysts require two categories of skills: ‘hard’ (technical) skills and ‘soft’ (employability) skills.
Technical skills for becoming a data analyst
Technical skills that all data analysts require include:
- Math and statistics: Solid knowledge of math, including calculus and algebra.
- Programming skills: Scripting languages like Python or R, and popular programming packages (such as those available on the Python Package Index or RStudio).
- Databases: Knowledge of data storage theory and software, such as Hive, Spark, and SQL.
- Microsoft Excel: Essential for transforming data and automating complicated analytical functions.
- Data visualization: Either a proprietary tool like Tableau, Python packages, or the graphing functionality in MS Excel.
Soft skills for becoming a data analyst
While data analytics is highly technical, ‘soft’ employability skills are even more invaluable. This is because they are both in-demand and hard to train. You’ll need to exhibit things like:
- Critical thinking: The ability to logically interrogate your data, assumptions, and biases. This may uncover errors and patterns others might not spot.
- Communication and collaboration: Data analysts rarely work alone but often collaborate with project managers, finance teams, marketing, sales, and C-suite executives. You must be able to communicate with all these different domains.
- Problem-solving: This involves taking a logical but creative approach to dealing with issues and hurdles, creating new processes to solve them.
- Resilience and agility: Data analysts must deal with setbacks and unexpected hindrances. You’ll need to be adaptive.
- Ethics: Working with data might seem fairly innocuous and introspective, but it often has real-world impacts. Having a strong social conscience and grasp of data ethics is paramount (more on this in the next section!)
- Curiosity: Eagerness to learn, absorbing new information and skills as the need arises. You might hear the term ‘growth mindset’ being thrown around—that’s the buzzword related to this soft skill.
3. Do you need a degree to get a job as a data analyst?
Having a degree was once a prerequisite for any high-skilled job. And sure, you’ll still need one to become a surgeon or an astronaut! But for data analytics, the story is less cut and dry. The world is changing fast, and in many contexts, degrees don’t hold the currency they once did.
Increasingly, employers are less interested in a piece of paper than by demonstrable 21st-century skills and work experience. While a degree may be the deciding factor if it comes down to two candidates with equal skills, it’s by no means a must for data analyst roles. Here are several reasons why:
1. Data analytics is a fast-evolving profession
A degree can take two or three years to complete. Meanwhile, data analytics is evolving at a dizzying speed. New roles are constantly emerging. Data analysts can now specialize in areas ranging from data engineering and database design to data visualization.
By the time you’ve graduated from a university course and received a degree, much of what you learned may already be outdated. This doesn’t devalue a degree entirely (areas like math and statistics will always be relevant) but does mean that degrees aren’t always as agile as some shorter qualifications.
2. Employers value diversity
Universities often perpetuate the institutional biases that prevent certain groups of people from thriving in their careers. Put bluntly, graduates in most western countries are disproportionately white and middle class. Although this may be a case of default rather than design, it’s still a big problem. It’s especially problematic for a field like data analytics, where so much power lies in the hands of algorithms that can absorb the unconscious biases of their designers.
Employers understand this—it’s why they are actively looking to diversify their workforces. By hiring beyond those with university degrees, employers not only increase opportunities for people from under-represented communities. They also start to mitigate the issue of unconscious bias in data analytics.
3. Jobs are changing in the 21st century
It used to be that individuals would pick a single profession for life. That’s no longer the case. Rapid social change and job automation are disrupting the careers landscape beyond recognition. Indeed, most people born in the 21st century will have three or four careers throughout their lifetime.
As such, employers can no longer feasibly demand a relevant degree every time someone changes career. To do so would vastly narrow the field of suitable candidates. It’s why employers now often look at a broader range of qualifications. Although this isn’t the case for every profession, it is broadly true for data analytics, a 21st-century job that’s highly adaptive to change.
4. The best data analytics training options
Finally, what are the best ways to train as a data analyst? Here are some options we recommend for getting started:
Free courses
Before forking out for a pricey course it’s probably a good idea to dabble in some more cost-efficient options first. Free online courses are a great way of seeing if data analytics is right for you. Start by watching some YouTube videos, or perhaps do a beginner tutorial in Python. There are also many free courses you can check out on sites like Coursera. These are generally limited in scope but they’ll give you a good taste of what data analytics involves.
An online certified program
Certified online courses are a popular option for those looking to get started. There are many available, so make sure you research the options to ensure the course covers the skills you’re looking for. Although certified programs aren’t free, they’re generally far cheaper than university degrees and are broadly recognized (check this before spending any money though).
Another benefit of certified programs is that they are much shorter than university degrees (weeks or months, rather than years). They tend to be delivered by real data analytics professionals, too, meaning everything you learn is up-to-date. Plus, you can fit study in around your other responsibilities. And if you work for a company that’s happy retraining you, many providers accept employer-funded scholarships.
For more information on certified programs, check out our handy guide: The Best Data Analytics Certification Programs
Graduate apprenticeship
Graduate apprenticeships combine conventional degrees with hands-on experience in the field. They’re growing increasingly popular for areas like data analytics, incorporating the formal knowledge of a traditional degree with opportunities to implement your skills in real workplace settings, usually via the university’s employer partners.
This is great if you’re keen to balance theory with practice. It’s also common for graduate apprentices to land their first job with their work experience employer. The main drawback is that it’s more expensive and time-consuming than a certified online program. The trade-off is that you’ll be working more or less from the word go. Graduate apprenticeships aren’t available everywhere yet, though, so be sure to check your local jurisdiction.
Conventional degree
Lastly, we’re not saying you shouldn’t get a degree, only that it’s not the only option! If you’ve got the money and time to spare and know exactly what you want, then we encourage it wholeheartedly!
A conventional bachelor’s, master’s or Ph.D is still a great option, especially if you’re interested in a highly-specialized area like data science, machine learning, or artificial intelligence. And if you opt for a certified program instead, that doesn’t rule you out of these fields—it just means you’ll be on a different route to get there.
Training checklist
Ultimately, we can’t tell you which training option is right for you. But this checklist can help you decide:
- Does your chosen course cover the topics you’re interested in? Perhaps you want to learn Python, or maybe you’re fascinated by data viz? Before splashing out, ensure the course covers everything you want to learn. Not all courses are created equal.
- Does the course combine theory and practice? Degree apprenticeships do this by default but any course should have some hands-on elements, even if just a portfolio project. Learn by doing, and avoid paid courses that are heavy on theory only.
- Is it pitched at the right level? Be sure to find a course that is complex enough to interest you, yet not so simple that you aren’t learning something new. Similarly, avoid an advanced course if you’re a complete beginner. If in doubt, send someone an email to ask.
- Does it have progression options? The best courses will prepare you to progress in your career. They will include interview support, resume checks, and general careers advice. Your chosen course should help you build the confidence you need to land that life-changing role. Good luck!
5. Wrap-up and further reading
Data analytics can be a fascinating and rewarding career. And while a formal degree is one way of getting into the industry, it’s by no means the only route to success. Experience and initiative are as valuable as the piece of paper you hold. With some formal technical training, you might be able to get started sooner than you think.
To learn more about a career in data analytics, sign up for this free, 5-day data analytics short course or check out the following introductory guides: