Probabilistic-Programming-and-Bayesian-Methods-for-Hackers VS PythonDataScienceHandbook

Also this is quite nice practical introduction which might help with finding answers to your questions: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

This is a good intro to probabilistic programming.

Probabilistic programming is a nice technique to have up your sleeve.

+1 for Statistical Rethinking. I’m also partial to Bayesian Methods for Hackers.

🔗 https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

You’re welcome! I would recommend Bayesian Methods for Hackers

All content is open-source: everyone is free to read, but also to contribute to the book using github. I know of one other book that followed this open-source 'publishing' model and became quite successful eventually through community efforts. I contemplated for a bit to create a book DAO but I think it's going to be overkill :).

Jake Vanderplas's Statistics for Hackers presentation is a perfect place to start. Bayesian Methods for Hackers is also very good.

Python Data Science Handbook

Python Data Science Handbook — learn to use Python libraries such as NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools to effectively store, manipulate, and gain insight from data

I don't know what tools you will be using but if you will be using Python you can start with Python Data Science Handbook by Jake VanderPlas and Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting DataData Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data which gives a very good outlook on the data science and big data frame work. PS: Jake's book is also available as jupyter notebooks so you can read and run the code at the same time.

Python Data Science Handbook by Jake VanderPlas (https://jakevdp.github.io/PythonDataScienceHandbook/)

Otherwise you can work through courses on Datacamp, Coursera, Udemy, etc, or check out this book for a more general non-spatial perspective.

7. Data Science Handbook Are you looking for a comprehensive guide to data science with Python? Look no further than the Data Science Handbook by Jake VanderPlas. This repository contains the entire book, which introduces essential tools and techniques used in data science, including IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. It’s a fantastic resource for anyone looking to deepen their understanding of data science concepts and best practices.