Nowadays, the data science field is hot, and it is unlikely that this will change in the near future. While a data driven approach is finding its way into all facets of business, companies are fiercely fighting for the best data analytic skills that are available in the market, and salaries for data science roles are going in overdrive.Companies’ increased focus on acquiring data science talent goes hand in hand with the creation of a whole new set of data science roles and titles. Sometimes new roles and titles are added to reflect changing needs; other times they are probably created as a creative way to differentiate from fellow recruiters. Either way, it’s hard to get a general understanding of the different job roles, and it gets even harder when you’re looking to figure out what job role could best fit your personal and professional ambitions.

The Data Science Industry: Who Does What

DataCamp took a look at this avalanche of data science job postings in an attempt to unravel these cool-sounding and playful job titles into a comparison of different data science related careers. We summarized the results in our latest infographic “The Data Science Industry: Who Does What”: In this infographic we compare the roles of data scientists, data analysts, data architects, data engineers, statisticians and many more. We have a look at their roles within companies and the data science process, what technologies they have mastered, and what the typical skillset and mindset is for each role. Furthermore, we look at the top employers that are currently hiring these different data science roles and how the average national salaries of these roles map out against each other.Hopefully this infographic will help you to better understand the different job roles that are available to data passionate professionals. If you want to learn more on data science, make sure to check out DataCamp’s interactive R and data science tutorials and join 220,000 data enthusiasts!Interested in some of our other infographics? Check out Statistical Language Wars, How to Become a Data Scientist in 8 Easy Steps and R vs Python.