After being dubbed by Harvard Business Review as “sexiest job of the 21st Century” in 2012, Glassdoor named it “the best job of the year” for 2016.

However, the stance towards data scientists has changed considerably over those four years: in 2012, the majority of articles focused on trying to explain what a data scientist is and what they do exactly. Back then, a short search on Google on the words “How to become a data scientist” showed that the concept had different meanings to different people. In 2016, this search still gives you a variety of articles and a broad range of opinions on the topic. However, whereas the data scientist used to be a person that could actually exist, more and more articles now focus on explaining why the data scientist is a unicorn.

Because there aren’t many yet that meet the high expectations that have been set, even though the definition of a data scientist is not fixed. Job postings show that companies are looking for people that possess communication skills, creativity, cleverness, curiosity, technical expertise, … The way that these capabilities are sometimes described makes it seem impossible for people to become a data scientist.

With the demand succeeding the supply, the trend of data science teams rather than data scientists is on the rise, and with it, a renewed strong focus on the what and the how of data science. However, just like the definition of a data scientist, the definition of data science is multi-faceted, and there is a lot of advice out there for those who want to learn data science. This information, however, can be industry- and context-dependent, and personal.