Data science is a mashup of skills ranging from computer science and statistics, to machine learning and strong communication. Which subskills exist within that range is a hotly debated topic, yet the material available is usually opinion, or the results of a survey.
Data science is a mashup of skills ranging from computer science and statistics, to machine learning and strong communication. Which subskills exist within that range is a hotly debated topic, yet the material available is usually opinion, or the results of a survey. These two sources are useful indicators, but they don’t do much to answer a very simple question: what are the most common data science skills?
To answer this question, we turned to the ultimate data source on professional skill sets – LinkedIn. We analyzed the profiles of over 11,000 self-identified data scientists, looking at 245,000 skill records to find the definitive answer to this question.
The full results of this research can be found in The State of Data Science.
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The top data science skills
While data scientists are known for their impressive abilities with statistical modeling and big data, the top of the skills list is one that is much more foundational – data analysis. Data analysis is a skill that shows up on the LinkedIn profiles of over 55% of all data scientists. It is then followed by skills like R, Python, and machine learning.
This list lines up with the skills companies are looking for as well. In The Must-Have Skills You Need to Become a Data Scientist, Linda Burtch gives her perspective as a recruiter on the top skills that are at the top of hiring managers’ lists. She lists the most sought-after skills as R, Python, SQL, SAS, and Hadoop, which are all found in the top 20 skills found on LinkedIn profiles.
Burtch adds,
Every company will value skills and tools a bit differently, and this is by no means an exhaustive list, but if you have experience in these areas you will be making a strong case for yourself as a data science candidate.
That’s exactly what LinkedIn’s skills section is about, proving that an individual would be a worthy candidate.
Top data science skills by level of experience
Just as a data scientist’s job is different from that of a software engineer, so too are the jobs of Junior, Senior, and Chief Data Scientists. In a Washington Post article titled, What Does It Mean to Be a Chief Analytics Officer?, Thomas Davenport explains one of the fundamental differences that sets Chief Data Scientists apart from the rest of the pack:
Another common factor among [Chief Data Scientists] is a strong emphasis on evangelism about analytics and what can be done with them. They are effectively sales reps for a different approach to decision-making. Many managers don’t understand either the basics or the subtleties about these topics.
Davenport’s commentary on Chief Data Scientists is backed up by what we saw in the data:
We found that Senior and Chief Data Scientists are less likely to report technical skills. Instead, they list skills like business intelligence, leadership, strategy, and management. These are all skills that one has to develop if they’re going to move into a role responsible for turning data-driven insights into strategic action.
Keep learning about data science trends
From skill sets to educational trends and hiring, data science is still a rapidly evolving field. Consider this, over half of today’s data scientists have only earned that title within the past four years. We expect that as the best practices are formalized and new technology to make sense of big data enters this market, this skills of a data scientist will likely evolve as well. Our research into the growth of data science turned up trends in educational backgrounds, the companies snapping up this desirable skill set, and where in the world data scientists are located. The full results of that research can be found in The State of Data Science.