git-internals-pdf VS PythonDataScienceHandbook

For me the Peepcode Git Internals book was a great peek under the hood. I went from "Git has a lot of magical incantations" to "Git is pretty simple and I could probably build a version of it".

I generally recommend the Peepcode Git Internals book. The first half explains the internals of how Git works, and the second half is a command reference.

Once you get the hang of basic Git operations, you should look into how Git works under the hood. Git Internals helped me a lot on this.

I often recommend the Peepcode Git Internals book. The first half talks about how Git works internally. The second half is a "how to use Git" tutorial. I think understanding the internals (which aren't really that complicated) can really help to demystify Git.

Conceptually, Git's not really all that complicated. I always recommend people to read the Peepcode Git Internals book (originally $9, now free): https://github.com/pluralsight/git-internals-pdf/blob/master/drafts/peepcode-git.pdf

Check out this page if you haven't yet (alternatively, direct link to the PDF.) I hear it's all good, but the Understanding Git chapter is the one I'd specifically point you to.

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.