A Python library for timeseries forecasting with very extensive features. The documentation also features some in-depth pedagogical explanations of how to properly forecast data and what methods can be used to improve results.

A useful guide for hyperparameter tuning of LGBM models. Mostly, if like me you always forget what parameter range is sensible, you can find it in there.

Online book on how to use command-line tools for quick data science results. This is for when your boss asks you about some statistics of your recent data output and you don't want to write a whole script for it.

An online book focusing on the fundamentals of data science (statistics, traditional machine learning). I don't know much about R (on which this book is based) but most of the theory in there is relevant for any junior data scientist.

A must-read for anyone beginning in data science. Chapter 5 features some great in-depth notebooks on classical machine learning methods like SVM, random forests, etc...