How to Become a Data Scientist

If you love data, complex problem-solving and the idea of long-term job security, a career in data science might be for you. Over the past decade, the veritable boom in big data has ensured that data scientist is one of the world’s fast-growing roles. In fact, according to the U.S. Bureau of Labor Statistics, data science roles will grow 22% by 2030—faster than the average for all other occupations.

The journey from entry-level data analyst to full-fledged data scientist won’t happen overnight, and it won’t always be easy. But if you love a challenge and want to invest in a rewarding career, data science ticks all the personal and financial achievement boxes. But what exactly does a data scientist do? How much can you earn working as a data scientist? And how would you even go about becoming one in the first place?

In this post, we’ll answer all your burning questions. By the time you’re done, you’ll be equipped with everything you need to decide if data science is the field for you. Read on, or use the clickable menu to jump to the topic of your choice:

  1. What does a data scientist do?
  2. Why become a data scientist?
  3. How much do data scientists earn?
  4. What is a data scientist’s typical background?
  5. How to become a data scientist
  6. Wrap-up and further reading

Ready to learn how to become a data scientist? Then let’s dive in.

1. What does a data scientist do?

First up, you’re not going to pursue a career without knowing what it involves, right?

What is data science?

An emerging, multidisciplinary field of study, data science blends big data, data analytics, statistics, and informatics with computer science and technology. Because it’s used in many industries (from medicine and finance, to retail and scientific research) this definition is necessarily broad. The devil, as they say, is in the detail: the nuances of a data scientist’s role lie in the idiosyncrasies of each position.

Ultimately, one thing bonds all data scientists, regardless of the specifics. Namely, they all identify patterns in big data, extract insights from these and use them to ask strategic ‘big picture’ questions to drive their organizations forward. Defining exactly how a data scientist identifies these patterns, and identifying what business progress looks like in their specific context is where things start to get murky. The answers here vary greatly depending on the industry and the role.

We’ve covered data science as a discipline in detail in this article. We also conducted a webinar with our own in-house data scientist, Tom Gadsby, going over the basics of the discipline.

How does data science relate to (and differ from) data analytics?

One key skill that all data scientists rely on is data analytics, which includes collecting, cleaning, storing, and mining big data. However, data scientists require more than data analytics skills alone. They’ll also be experts in a particular business domain—from pharmaceuticals to software programming—and will be expected to carry out tasks that data analysts won’t. These tasks might include creating complex machine learning algorithms from scratch, deploying and managing vast data warehouses, building deep learning infrastructures, and communicating the results of their work to various stakeholders, from C-suite professionals to product teams.

Although the term ‘data scientist’ is pretty hot right now, the truth is that the role is relatively new and constantly evolving. As innovative technologies streamline data scientists’ most time-consuming tasks, fresh skills and expectations are surging in to fill the space at an astonishing pace. Considering that data science is all about making predictions, this lack of predictability about the profession’s future is, ironically, what draws many to the field in the first place. In short, a career in data science certainly won’t be a boring one!

Read more: Data Analytics vs. Data Science: What’s the Difference?

2. Why become a data scientist?

Becoming a data scientist isn’t the most straightforward career path, which is partly due to the fact that there are many routes into the field. As we’ve seen, even defining exactly what data science involves isn’t all that easy!

For many professionals, data science is necessary to their broader work. For instance, for research specialists in fields like physics, cognitive science, or astronomy—or any scientific discipline for that matter—data science is an integral part of their studies.

But there’s a second, emerging group that we could loosely term ‘career data scientists’. This describes those who have actively chosen to pursue a career in the field, not because it forms part of their broader work, but because they love working with data, love problem solving, and want to forge a career that adds real value to a business or industry.

Given the hurdles we’ve to overcome to get here, though, why bother pursuing a career in data science in the first place? Here are a few compelling reasons.

There’s a shortage of data scientists

Over the past few years, global demand for data scientists has exploded. Simultaneously, though, we’ve seen massive shortages of qualified professionals available to fill emerging roles. A big push from government and businesses to promote STEM jobs, especially among women, is gradually changing this. Nevertheless, the staggering pace of growth is still outstripping supply. According to insights company Statista, the global data science market is forecast to grow to $103 billion by 2027—this is double its predicted size in 2018! There’s still a shortage of qualified people, though, so now’s the time to seize an opportunity to enter the field.

Good data scientists become valued experts in their organizations

They say the best way to become an expert in your field is to work in a very narrow field. Data science fits the bill perfectly! If your job adds value to an organization and you’re one of the only people who can carry it out, then you have potentially secured yourself a job for life. Of course, getting to this point means developing the prerequisite data science skills you’ll need, such as data analytics, statistics, and business know-how. You’ll also have to become a domain expert for your industry or sector. While this is hard work initially, it offers long-term payoffs. You’ll not only have a measurable impact on the business—you’ll become irreplaceable in your role.

Data scientists get to develop broad skill sets

Beyond knowledge of data analytics, statistics, and computer science, data scientists aren’t limited by particular boundaries. Being so unique, each data science position offers a rare opportunity to shape your role. Interested in machine learning? Then find a way to weave it into your work. Keen to work in a particular sector? So long as you’ve got the fundamental data skills nailed, we’re going to take a punt and say you’ll be able to break into your preferred industry. Our world faces massive challenges and data science is one of just a few emerging multidisciplinary fields that cultivate high-value, transferable skills that are guaranteed to be relevant in the future. So don’t miss the boat.

Data scientists can work remotely (mostly!)

One benefit of the pandemic has been the normalization of remote working. We are no longer geographically restricted by the opportunities available to us. If you want to work in Silicon Valley from the comfort of your apartment in New York or Berlin, there’s nothing stopping you. Some organizations may still prefer candidates close to their headquarters, but if you’re a talented data scientist, you’ll be in demand. And this gives you great leverage to negotiate the particulars of your contract (remote working being just one of these).

Data scientists earn well

We all know what high demand and low supply mean: if you’re half-decent at your job, you’re guaranteed to get paid a decent salary. And that’s a great segue to our next section…

3. How much do data scientists earn?

A job well done is its own reward, right? Perhaps that’s partly true. But we’ve still got bills to pay, so show us the money! 

How much can a data scientist earn? Fortunately, as an in-demand role, data science pays a pretty comfortable salary. Sourcing data from several different job sites, we can deduce the average salary for a data scientist in the U.S.—let’s take a look:

  • The average U.S. data scientist salary according to Salary.com is $72,000
  • The average U.S. data scientist salary according to Indeed is $75,000
  • The average U.S. data scientist salary according to Payscale is $97,000
  • The average U.S. data scientist salary according to LinkedIn is $105,000
  • The average U.S. data scientist salary according to Salary Expert is $116,000
  • The average U.S. data scientist salary according to Glassdoor is $117,000

These are just estimates, of course, but they do offer us a baseline. Taking an average of these figures, the approximate salary for a data scientist in the U.S. is $97,000. Naturally, this doesn’t consider contributing factors like experience, job title, industry, and location. To dig into the details here, see our full post on this topic: What is the average data scientist salary?

Presuming you’re still early in your data career, at this stage, you might be more interested in learning how much a data analyst can earn (a role that is less senior than data scientist). Find out in our ultimate data analyst salary guide.

4. What is a data scientist’s typical background?

Data scientists neither come from a single background, nor do they have a single career development pathway available to them. What this lacks in definition, it more than makes up for in flexibility! Nevertheless, people will still try to place data science into neat boxes. This is because, in the past, it was common for highly-skilled roles to follow the same route: go to college, get a degree in a given field, and then enter a job. 

However, in our rapidly evolving, high-tech age, this model is increasingly outdated. Data science is representative of a new type of 21st century role. Instead of following a prescribed career path, this sees data science as more of a spectrum of skills that you can guide in any direction of your choosing, and build into a career that you have a direct hand in shaping.

All this said, for the sake of keeping things straightforward, computer scientist Joel Quesada has highlighted a three-way division of data science skills into the following helpful categories:

  • The analytical data scientist
  • The IT data scientist
  • The research data scientist

Let’s look briefly at each of these now.

The analytical data scientist

Analytical data scientists usually have a background in math. They may have previously been a statistician, physicist, mathematician, or economist. Essentially, if you’re great with numbers, enjoy problem-solving with algebra and calculus (but are perhaps less interested in dealing with things like big data or complex modeling) then this could be the path for you. Of course, you’ll still need a good grasp of basic analytics tools like Python, Excel, and SQL.

The IT data scientist

IT data scientists usually have a background in software engineering or computing technology. Coders at heart, this type of data scientist usually emerges from fields like software engineering, information systems, and similar. If you enjoy the knottier aspects of programming, love learning new languages, creating APIs, working with IT teams, and creating complex data models, then you may well thrive as an IT data scientist.

The research data scientist

Research data scientists are primarily interested in cutting-edge technologies. They may have a background working in labs, as traditional scientists, or in academia, and usually have a Ph.D. If you’re fascinated by the potential of emerging tech like artificial intelligence, deep learning, and creating highly specialized algorithms for solving real-world problems, then this could be the pathway for you.

The distinction between these three types of data scientists isn’t 100% clear-cut. However, they do offer a flavor of how different data science roles can differ. In our quest for a clear definition of a (relatively!) nebulous concept, this is undoubtedly the best we’ve found so far.

5. How to become a data scientist (step by step)

Okay, so we’ve determined what data scientists do, how much they can earn, and what sort of background they tend to come from. Next, how do you become a data scientist? Presuming you’re completely new to data, here’s our step-by-step guide to how to become a data scientist for real.

1. Get certified as a data analyst

Whether you’re a software developer or mathematician, you’ll still need to learn the fundamental skills required to work in data science. We’ll presume this is your first time dipping a toe into data. In this case, you’ll ideally have an undergraduate degree (although it doesn’t necessarily need to be in a field related to data science).

You’ll probably want to sign up for a data science bootcamp or another certified program to learn the skills you need and to receive an industry-recognized certification. The skills you’ll learn will likely include Python, SQL, statistics, data cleaning, and exploratory data analysis. 

2. Choose an entry-level pathway

Perhaps you have a background in IT or the sciences. If so, you may want to pursue data science through these pathways. If not, now might be the ideal time to find an entry-level data analytics job. We don’t recommend securing any old role but if your ultimate aim is to move into data science, try embracing everything as an opportunity. 

Cast the net wide and keep an open mind about the industry or role that you’d like to work in. Common industries for data analysts include finance, healthcare, IT, and government. Don’t fear if your first job isn’t your dream job. Every role is a stepping-stone. At this stage, just learn what you can and find out which industries or specialisms interest you most.

Read more: The Ultimate Guide to Entry-Level Data Analyst Jobs

3. Get a degree

While it’s not strictly necessary to have a degree to land an entry-level data job, you’ll struggle to proceed in data science without one. This might be a data-related undergraduate degree, a Master’s, or—if you want to pursue a career as a researcher—a Ph.D. 

Perhaps after a year or so of working in data analytics, you’ll identify an interest in IT systems. Or maybe you’ll be particularly enamored by data science in the healthcare sector. Whatever your interests, use what you’ve learned so far to pursue a qualification that builds on your existing strengths and directs you towards your data science goals. Your degree could be in a subject like math, statistics, or computer science, or it could be in a domain area like accounting, finance, or business management. 

4. Acquire as many new skills as possible

Wherever you are on your journey, do whatever you can to pick up new skills. If you’re keen on working as a research scientist, for example, perhaps dabble with some machine learning libraries, like PyTorch or TensorFlow. Or maybe, although you have a background in math, you’re increasingly fascinated by software systems. If so, grab every opportunity to work alongside IT or to build your coding expertise with programming languages like Python, JavaScript, and R. Even if you learn a new skill that’s not for you, you’ve still learned something. Even skills you don’t love look great on your resume.

5. Make a list of your favored organizations/industries

As your career progresses, make a list (mental or otherwise) of organizations you’d like to work with. Alternatively, list particular industries that interest you, such as finance; or an area of data science, such as data engineering. 

Once you’ve made your ‘dream list’, target industries or companies that are ahead of the curve in the field that grabs you most. You’ll need to start building experience in these areas. To do that, keep an eye on company job pages, send speculative applications (if appropriate), and network with other data science professionals to find new contacts.

6.  Make yourself indispensable

Data scientists are in-demand but don’t forget: not all data scientists are good data scientists! Stand out from the crowd by making yourself indispensable. 

The more niche skills you can pick up, the more you can personalize your role, and the better the recommendations or suggestions you can provide. All this will increase how indispensable you are. The more valuable an organization finds you, the more they will need your skills and the more money you’ll ultimately be able to earn.

6. Wrap-up and further reading

There we have it! Our complete guide to how to become a data scientist. As this post highlights, data science is not so much a job description as a multidisciplinary field that has the potential to be sculpted into any number of possible careers.

As long as you have—or are willing to obtain—the necessary prerequisite skills (including a degree, a data analytics certification, and knowledge of your business domain), then you can carve yourself a niche and successful career in almost any sector of your choosing. The path into data science is not a prescribed one. While this means it’s not always straightforward, the rewards of working in this area more than redress the balance. Good luck!

To learn more about how to get started in data analytics or data science, check out this free, 5-day data analytics short course, or check out the following posts:

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