Data science is a horribly overloaded term. Ask a databases person and they will tell you about ETLs and relational joins. Ask a statistician and you will hear

Data science is a horribly overloaded term. Ask a databases person and they will tell you about ETLs and relational joins. Ask a statistician and you will hear about estimators and the bias-variance tradeoff. Ask a social scientist and they will tell you about experiments and causal inference. Ask a CS theoretician and they will mention streaming algorithms and probabilistic guarantees. Ask a machine learning person and you will probably hear a lot about prediction and stochastic gradient descent. The approach that will breed the best practitioners is an interdisciplinary one; a great example is UBC's program: https://masterdatascience.science.ubc.ca/program/courses https://masterdatascience.science.ubc.ca/program/courses. And also future positions that separate the roles of data janitor, data analyst, machine learning engineer, and so on. There is a huge difference between what Facebook's Core Data Science team does and what most companies call "data science", which is sitting on a huge pile of poorly aggregated data and having little idea what to do with it. Most importantly, data science has to be thoughtful and part of the engineering process, and can't be done after the fact with digital trace data. It's a lot easier to run experiments than it is to do statistical gymnastics with observational data.