Because big data and data science are here to stay, this chapter explores key features of data scientists, types of data scientists, and how to become a data scientist, including training programs available and the different types of data scientist career paths.
There are a few key features of data scientists you may have already noticed. These key features are discussed in this section, along with the type of expertise they should have or acquire, and why horizontal knowledge is important. Finally, statistics are presented on the demographics of data scientists.
Data scientists are not statisticians, nor data analysts, nor computer scientists, nor software engineers, nor business analysts. They have some knowledge in each of these areas but also some outside of these areas.
One of the reasons why the gap between statisticians and data scientists grew over the last 15 years is that academic statisticians, who publish theoretical articles (sometimes not based on data analysis) and train statisticians, are… not statisticians anymore. Also, many statisticians think that data science is about analyzing data, but it is more than that. It also involves implementing algorithms that process data automatically to provide automated predictions and actions, for example: