It’s no secret that data scientists still have one of the best jobs in the U.S. With the amount of data growing exponentially, the profession shows no signs of slowing down. Data scientists have a base salary of $117,000 and high job satisfaction levels, and according to the LinkedIn Workforce Report, there is a shortage of 151,717 data scientists in the U.S. alone.

One explanation for this shortage is that the data scientist role is a relatively new one and there just aren’t enough trained data scientists. That’s why we at 365 Data Science set out to discover what makes the "typical" data scientist. We aimed to dismantle the myths surrounding the role and inspire some of you to enter the field when you otherwise may have felt like you wouldn’t fit the criteria.

About the study

We examined 1,001 LinkedIn profiles of people currently employed as data scientists, and then collated and analyzed that data. Forty percent of the professionals in our sample were employed at Fortune 500 companies and 60% were not; in addition, location quotas were introduced to ensure limited bias: U.S. (40%), UK (30%), India (15%) and other countries (15%). The selection was based on preliminary research on the most popular countries for data science where information is public.

We carried out the first instance of this study in 2018, when we discovered data scientists have a wide range of skills and come from an assortment of backgrounds. You can see what skills the typical data scientist used to have here.

The tech industry and business needs are constantly changing entities; therefore, data scientists must change with it. That’s why we decided to replicate the study with new data for 2019. Of course, there were plenty of insights — which we will discuss in depth — but first, let's a take a quick look at a overview of the typical data scientist.

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At first glance, we see that the data science field is dominated by men (69%) who are bilingual, and they prefer to program in Python or R (73%); they have worked for an average of 2.3 years as data scientists and hold a master’s degree or higher (74%).

But is this what you must embody to make it as a data scientist? Absolutely not! As we segment the data, we get a much clearer view.

Does where you went to university make a difference?

In a profession with a six-figure salary and fantastic growth prospects, you wouldn’t be blamed for thinking that Harvard or Oxford graduates' résumés are the ones that find their way to the top of the pile on the desk of any hiring manager.

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Note: The categories are uneven.

But that’s not the only conclusion we can draw. You can see that a large part of our cohort attended a Top 50 university (31%). We used the Times Higher Education World University Ranking for 2019 to estimate this. But before you lose hope, notice that the second largest subset of data scientists is comprised of graduates of universities ranked above 1,001 or not ranked at all (24%).

That said, it seems that attending a prestigious university does give you an advantage — hardly a surprise — but data science is far from being an exclusive Ivy League club. In fact, data science is a near-even playing field, no matter which university you graduated from.

So, the data shows that a university’s rank doesn’t greatly influence your chances of becoming a data scientist. But what about your chances of getting hired by a company of specific size? Does a university’s reputation play a role there?

Let’s find out!

Are employers interested in where you went to University?

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Great news! The tier of university a data scientist attends seem to have close to no effect on his or her ability to find employment at companies of different sizes. Data scientists are valued everywhere, from Fortune 500 companies to tech start-ups. This reinforces the idea that a data scientist is judged by professional skills and level of self-preparation.

That said, almost half our cohort earned at least one online certificate (43%), with three being the average number of certificates per person. It’s worth mentioning; however, we believe these numbers are higher in reality — many people do not list certificates that are no longer relevant, even if they would have been beneficial at some point. Think how unlikely it would be for an experienced senior data scientist to boast about a certificate in the fundamentals of Python.

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Self-preparation is a huge factor in gaining employment, but is there any correlation between the rank of the university you graduated from and whether you took online courses?

Which graduates are more likely to take online courses?

Our assumption was that only be students from lower-ranking universities had to boost their résumés with skills from online courses. But perhaps the data tells a different story.

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Indeed, it does.

The Top 500 ranked universities are split between our first five clusters. These show similar numbers of graduates who have taken online courses: 39%, 38%, 40%, 39%, and 42%. These percentages are not far from the overall percentage of data scientists in our cohort who report earning a certificate (43%). The 501-1000 cluster does show 55%, which is somewhat higher and may support the notion that graduates from lower ranked universities need more courses. However, when we reach the "not ranked" cluster, the number (47%) is closer to the average.

These results show that self-preparation is popular among graduates from all universities and incredibly valuable when preparing for a career in data science.

Note: The 1,001+ cluster contains only seven people, which isn't a large enough sample to gain reliable insights. Therefore, we won’t be discussing these results.

Conclusion

If these results show us anything, it’s that the field of data science is fairly inclusive. As long as aspiring data scientists put in effort to develop their skills, they have a shot at success. While many top careers value a rigid (and sometimes elitist) path to success, data scientists are offered much more flexibility and freedom.

This article focused on university rankings in particular to exemplify this point, whereas the full study offers many more specific insights. If you’re keen to learn more, you can check out the full study here or even compare the findings with last year’s study.

Iliya Valchanov
Author
Iliya Valchanov

Iliya is a Finance graduate with a strong quantitative background who co-founded 365 Data Science, an educational start-up focused on data science. Since then, he has been creating online courses focused on statistics and machine learning.