awesome online books
Statistics
https://otexts.org/fpp2/ Forecasting: Principles and Practice, Rob J Hyndman and George Athanasopoulos
http://r4ds.had.co.nz/ R for Data Science Garrett Grolemund Hadley Wickham
https://adv-r.hadley.nz/ Advanced R Hadley Wickham
https://www.tidytextmining.com/ Text Mining with R A Tidy Approach Julia Silge and David Robinson 2018-04-02 (last update)
https://nbviewer.jupyter.org/github/unpingco/Python-for-Signal-Processing/tree/master/
http://www.burns-stat.com/documents/books/the-r-inferno/
https://rud.is/books/21-recipes/
http://www.burns-stat.com/documents/tutorials/impatient-r/
https://rud.is/books/drill-sergeant-rstats/
https://rud.is/books/creating-ggplot2-extensions/
http://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/index.html
https://lectures.quantecon.org/py/index.html
https://www.kevinsheppard.com/teaching/python/notes/
http://moderndive.netlify.com/6-regression.html
http://www.gastonsanchez.com/r4strings/
https://www.econometrics-with-r.org/
http://www-math.bgsu.edu/~albert/bcwr/
http://statmath.wu-wien.ac.at/~zeileis/teaching/AER/
https://cengel.github.io/R-data-wrangling/index.html
https://little-book-of-r-for-multivariate-analysis.readthedocs.io/en/latest/index.html
https://developers.google.com/machine-learning/crash-course/prereqs-and-prework
https://www.ime.unicamp.br/~cnaber/Ensino.htm
https://bookdown.org/yihui/rmarkdown/
https://principles.tidyverse.org/
https://advanced-r-solutions.rbind.io/
http://neuralnetworksanddeeplearning.com/
https://christophm.github.io/interpretable-ml-book/
https://bookdown.org/ccolonescu/RPoE4/
https://smac-group.github.io/ts/
https://www.ssc.wisc.edu/~bhansen/econometrics/Econometrics.pdf
https://jakevdp.github.io/PythonDataScienceHandbook/index.html
Patrick Landreman: A Crash Course in Applied Linear Algebra | PyData New York 2019
- https://www.youtube.com/watch?v=wkxgZirbCr4
- https://github.com/plandrem/PyData-2019
- http://vmls-book.stanford.edu/
- http://ee263.stanford.edu/
- https://www.youtube.com/playlist?list=PL06960BA52D0DB32B
Stanford CS229: Machine Learning | Autumn 2018
- https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
- http://cs229.stanford.edu/syllabus-autumn2018.html?utm_campaign=Data_Elixir&utm_source=Data_Elixir_283
Practical Python Programming
Introductory Econometrics for Finance
- Code & Datasets: https://www.cambridge.org/br/academic/subjects/economics/finance/introductory-econometrics-finance-4th-edition
- Python: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3475303
- R: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3466882