Learn Data Science Infographic

Learn Data Science Infographic

The stance towards data scientists has changed considerably over the past 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, DataCamp created the Learn Data Science Infographic providing an updated view of the eight steps that you need 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 still 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.