What Do You Need to Become a Data Scientist in 2020?


What Do You Need to Become a Data Scientist in 2020?

Hi, and welcome to another Data Science Special. This blog is not here to give an overview of Data Science as a super-smart career decision. Instead, we’ll look into what makes a data scientist successful in 2019! First things first – what is a data scientist? Well, the term was first used by DJ Patil and Jeff Hammerbacher in 2008, around the same time that the words swag and chai latte made it to the Oxford English Dictionary.


It’s safe to say that while these two are fairly easy to define, data science is a little a bit harder to understand. Essentially, the field deals primarily with data, no joke. A data scientist can take the data – small or big – and start developing, implementing, and deploying machine learning algorithms. They use advanced statistical methods to do predictive analytics and get meaningful insight from the data.

Often, the data scientist will also dabble in deep learning making use of the latest tech developments, such as neural networks and the likes. Obviously, all of this requires a fairly mathematically inclined thinking style and a lot of programming skills. But is this all? What about their education, work history, formal qualifications, and… of course, which industries hire data scientists in 2019 and pay them a base salary of $117,000? Let’s reverse-engineer the data scientist and find out! If you want to find what makes a data scientist – think like one. Did we conduct a study – methodology in the description – to find out more about the typical data scientist of 2019? At a glance, there’s a certain type of professionals who should definitely stay on board? The domain is still strongly dominated by men (69%), who can hold a conversation in at least two languages. They have been in the workforce for 8 years, but only working as data scientists for 2.3 of them.

They can proudly frame up a second-cycle academic degree (74% hold a Master’s or a Ph.D.), and do a lot more than program “Hello World” in Python or R (73%), often both. Luckily for those, who are female, or have not yet earned our Doctorates, the segmentation of the data tells a richer and truer story. So, you might be wondering does data scientist imply Doctor of Philosophy? And the answer is simple. Just as the field is not impregnable by women, so is having a Ph.D., not a prerequisite for the position. In fact, less than a third of the data scientists in the cohort hold a Doctorate degree (28%).

Data Scientist:

This is a comparable number to last year’s 27%, which seems to entail that industry does not intentionally introduce an unattainable degree of academic prowess. So far, so good for 2019! On the other hand, if the Master’s degree is something into which the aspiring data scientist is willing to invest time and effort, it seems to be the golden standard for academic qualifications (46% of the sample hold a Master). In fact, we can speculate that in the future the requirement for a second-cycle academic degree will decrease, being evened out by data scientists penetrating the field with only a Bachelor’s. And there is already a 4% increase compared to last year in the number of data scientists with only a Bachelor’s degree. Actually, unless you’re coming straight from academia, having a Ph.D. or master’s is not essential. Especially if you can land an internship (8%) or come from an IT background (9%).

What’s interesting, though is that there a lot of other gateways to a job as a data scientist. For example, academic researchers (9%), data analysts (13%), and consultants (6%) all have pretty respectable chances of being data scientists in 2019! And if you are to take a look at what you need to study to become a data scientist, you’d notice that the gateways to data science are very many. From Economics and social sciences (21%), through natural sciences (11%), statistics and mathematics (16%), computer science (22%), engineering (9%), and of course, data science and analysis (12%).

There was even one person who studied Law in our cohort! That’s all super interesting but do you still have to go to an Ivy League college or…? Actually, not at all. University ranking doesn’t seem to influence your chances of becoming a data scientist in 2019. While a high number of the data scientists in our research indeed come from the Top 50 universities (31%; according to the Times Higher Education Ranking for 2019), almost as many come from universities ranked above 1001+ (24%). So, Data Science is definitely not a private playing field for Ivy League graduates. However, one thing almost half of our data scientists have in common is online courses. 43% of them said they have gained at least 1 certificate from an online course, with 3 being the average. E-learning is definitely a resource many data scientists take advantage of it.

And it checks out – some of the best online programs out there are dedicated to helping you master various programming and data handling skills, which, after all, is the bread and butter of the data scientist. But we still haven’t talked about that, have we? Alright, so Python (54%) is definitely leading the pack when it comes to programming skills the 2019 data scientist needs, and that is universal all around the world. R (45%) comes second but in the UK it’s definitely lagging behind in popularity. SQL (36%) and MATLAB (19%) also prove to be widely considered handy, so you might want to invest some time brushing up on these, too, if you’re considering joining the data science race this year. And in which industry should you expect to be spending your glory days as a data scientist?

This was definitely easier to answer last year because the data was heavily pointing to the Tech industry as the titan hiring the most data scientists. 2019, however, is introducing a lot more diversity as is the trend with a lot of other things. That said, the Tech/IT industry (43%) is still a major employer but the industrial sector is rapidly catching up (39%). What’s even more interesting is that in the UK more scientists work there (38%), instead of in Tech/IT (35%). The financial sector is claiming 16% of the data scientists worldwide, with the UK again leading the pack (24%). No surprises here – London is (or used to be, a) Europe’s financial capital and plenty of financial, trading, and brokerage firms reside there. In that respect, the US stands at the humble 9%. The job market for data scientists there mostly employs data scientists in the Tech/IT sector (47%). But perhaps the most important question we can answer here is which location leads to the fastest career progression? Based on our data, India and the UK are the two locations where a data scientist can thrive even with little to no experience.

The States are the least accommodating to new data science enthusiasts and prefer professionals with 3 to 5 years of experience. Well, now you know where to look if you want to get promoted from already being in a data scientist position! So, what’s the key take-home message here? Hopefully, this blog does not make you doubt whether the data scientist profession is something you could realistically pursue. Instead, we hope to have lent a reassuring hand.

One of the main messages we extracted from our study both last and this year is that if you have the skill base that makes a data scientist, you can be a data scientist. It will be interesting to see how the data science profession changes in the next 2-5 years, but right now, a universal data scientist the profile appears to be taking shape: a unique programming language toolbox desired across industries and locations; preferably a Master’s degree or a Bachelor’s and proof of practical abilities; and a confident learning-on-the-go attitude are the currencies of the field. If you are interested in a solid data science preparation starting from scratch.

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