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Learn Data Science: Eight (Easy) Steps

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.

To guide you through this jungle of information and advice, our friends over at DataCamp have produced the “Become a Data Scientist in 8 Steps” infographic providing a view of the eight steps that you need to to through to learn data science. Some of these eight steps will be easier for some than for others, depending on background and personal experience, among other factors. The goal, however, is to make this a visual guide for everyone that is interested in learning data science or for everyone that has already become a data scientist or part of a data science team but wants some additional resources for further perfection.




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