I generally agree with you. If you want classical algorithms (which are, imo, fundamental), you can't go wrong start with Skiena's "Algorithm Design Manual" or

I generally agree with you. If you want classical algorithms (which are, imo, fundamental), you can't go wrong start with Skiena's "Algorithm Design Manual" or lecture notes: https://www3.cs.stonybrook.edu/~skiena/ https://www3.cs.stonybrook.edu/~skiena/ > the SciPy ecosystem, Jupyter Notebooks These, though, are gems that can be dramatic quality-of-life improvements for all kinds of data analysis. I resisted for a while (writing code in a web browser? BARF), but have gradually come to have a deep affection for pandas, numpy, scipy, seaborn, etc. They're quirky, their APIs are sometimes a bit strange and unintuitive, but they make data analysis tasks go so much quicker. I'm not a data scientist; most days I'm more of a systems programmer. Being able to take log files, write a bit of Python glue to transform them into Pandas Dataframes, and then slice-and-dice-and-plot different facets of the data rapidly is a game changer.