If you have recently browsed job boards and considered potential career paths, you probably know that the “data scientist” position is one of the most exciting opportunities in the market. Many people find this profession attractive, as it comes with several key advantages: salary, career opportunities, personal development, intellectual challenges, and multiple companies competing for few available candidates.
This sounds great, but how does a person become a qualified applicant for a data scientist position?
To understand this better, we conducted our “1,001 data scientist LinkedIn profiles” research for the third year in a row. Now, we are not only able to delineate the typical traits of data science professionals in 2020, but we can also compare this data with the 2018 and 2019 figures.
In 2020, our study portrays a data scientist’s collective image as a male (71%) who is bilingual and has been in the workforce for 8.5 years (3.5 years of which have been as a data scientist). He or she works with Python and/or R and has a Master’s degree.
For the third year in a row, the majority of data scientists in the research is male. The proportion remained very stable -- 70%-30% in 2018, 69%-31% in 2019, and 71%-29% in 2020 -- and is likely a true representation of the actual situation in the workplace. Nevertheless, the expectation is that, as the profession becomes more established, the gender gap should reduce in the years to come.
If we have to delineate three programming languages data scientists most frequently use in 2020, these would be Python (73%), R (56%), and SQL (51%). Besides the top 3, some other popular coding languages are MATLAB (20%), and Java (16%). In fact, this insight is the most actionable from the whole study. It gives people interested in a data science career clear evidence on where to focus.
At the moment, Python appears to be the data scientist’s preferred tool for data processing and problem-solving. Three years ago, things looked a bit different. In 2018, Python and R had the same level of adoption, according to our research (53%). Then, the tide turned in 2019 when Python came in the lead with 54% vs 45%, and now, we can see that Python established itself as the industry’s coding language of choice, with a significant lead over R.
Another interesting finding in 2020 is that fewer data scientists are in their first year on the job (13%) compared to previous periods (25% in 2018 and 2019). A few years ago, as data science had just emerged, companies were recruiting professionals with different backgrounds and training them in-house. As a result, in some cases, relatively junior candidates were hired for senior data scientist roles. Our numbers show that, as more people gain experience in the field, first-year data scientists account for a smaller portion of the total.
The idea that experience plays a bigger role in recruiting is reinforced by the finding that the average data scientist professional in 2020 has been in the workforce for 8.5 years, while our 2018 data showed an average working experience of 4.5 years.
Therefore, in today’s job market, one needs to accumulate the necessary working experience in an analytical position before they are ready for a data scientist job title.
Our study examined data scientists’ previous job occupation 1 and 2 jobs ago. Two positions prior to their current role, the average data scientist in our sample was either already a Data Scientist (29%), an Analyst (17%), or in Academia (12%). The figures change (but the roles stay the same) when we look at the positions our cohort occupied immediately before entering their current role: 52% for Data Scientists, 11% for Analysts, and 8% for Academia.
Sometimes, starting with an internship can be a valid strategy: 11% of data scientists were interns 2 jobs ago, and 7% of them were interns immediately before becoming data scientists.
What are the takeaways here? Python is currently the programming language of choice; many data scientists now have prior experience in the data scientist role; and those who have not worked in this role should gain analytical experience before aiming for the data scientist job title.
In terms of education, the large majority (95%) of current data scientists have a Bachelor’s degree or higher. Out of those, 53% hold a Master’s degree, and 26% - a Ph.D. We can say that a person needs to aim at a second-cycle academic degree; however, it is also true that a Bachelor’s can get you the job, as long as you have the technical skills and preparation required.
In general, 19 out of 20 data scientists have a university degree. But what area of studies did they pursue? Which degrees improve a candidate’s chances of becoming a data scientist?
Considering our study, 55% of the data scientists in the cohort come from one of three university backgrounds: Data Science and Analysis (21%), Computer Science (18%), and Statistics and Mathematics (16%). There are fewer representatives of Economics and Social Sciences (12%), Engineering (11%), and Natural Sciences (11%). All of these are technical courses that prepare graduates for the quantitative and analytical aspects of the job.
Based on the conducted research, we can summarize the following important findings describing the typical data scientist career path in 2020:
They say that “if you don’t know where you are going, any road will take you there.” In this case, things are a bit different. If you know that you want to become a data scientist, it will be beneficial to study the profiles of others who have taken the data scientist career path and learn from their experience. Good luck following in their footsteps!
You can find the full research for 2020 here.
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Nedko Krastev is a co-founder at 365 Data Science. He earned a Master degree in Finance from Bocconi University in Milan, Italy. In his professional path so far, Nedko has worked with companies like PwC (Italy), Infineon Technologies (Germany), and Coca-Cola European Partners (United Kingdom). Six years ago, Nedko started teaching online and then co-founded 365 Data Science, which established itself as one of the most popular educational brands online, having taught more than 750,000 students residing in 210 countries around the world.