10 Required Non-technical Skills for a Data Scientist

“Data Science(DS)” is nothing new but the term itself and the recent level of interest in it. As a practice it has commercially (not academically) existed for more than 25 years, mainly under “Data Mining (DM)” and “predictive analytics(PA),” since early 1990’s. DM and PA got a lot of traction originally in financial, Telco, and retail industries that had a lot of granular historical data. Like anything that gets sudden attention and interest, DS has been misused and abused in a variety of ways. Given the fast surge in market demand in the last several years, many claim to be or want to be data scientists. True data scientists and DS managers who had to deal with screening DS resumes, can testify to the level of present noise (false positives) in that application process.

“Data Science” tries to be an umbrella field that covers more of what data mining and predictive analytics practices have covered. That is justified since with the growth of data of all kinds in recent decade and what is expected in the coming years, we need a lot more of the people with relevant DS skill sets. The challenge however has been the definition of that “skillset.