> P.S. It's called ’statistics’. No, it's not. Read Breiman's "Statistical Modeling: The Two Cultures." http://www2.math.uu.se/~thulin/mm/breiman.pdf http://w

> P.S. It's called ’statistics’. No, it's not. Read Breiman's "Statistical Modeling: The Two Cultures." http://www2.math.uu.se/~thulin/mm/breiman.pdf http://www2.math.uu.se/~thulin/mm/breiman.pdf "Statistics" has largely been concerned with the "data modelling culture" Breiman talks about; a lot of data science is focused on algorithmic modelling, things like neural nets, random forests, and so on. A lot of these techniques have been refined outside of modern statistics because of statistic's focus on data modelling. This also ignores all of the things that fall outside of the purview of the modelling steps altogether, things like data cleaning, data engineering, and so on. All of those are properly "data science" but often fall outside of what's in a statistics textbook. If you are going to do data science, you should know statistics. You should know a lot of it. But that is far from the only thing you should know. As to your larger point... yeah, well, jobs allow people to eat and get health insurance and all that, so it's understandable that OP might want to be able to do those things and not just apply it for his own pleasure. My take on that is that it's hard, both to acquire the skills needed and to signal to employers that you have them. If you're going that route, you need to build a solid portfolio of work. Kaggle might be a good place to start.