Now that you have your Git reference book, you could probably use this shorter pocket guide for most of your day-to-day work.
Data Science Reading List – Intermediate
These books from my Data Science Reading List are for people who are beyond introductory texts and want more in depth information.
This book is a really well written and structured introduction to the main machine learning techniques. Every technique is supported by real coded examples on real datasets.
Read this book to whet your appetite for all things machine learning.
Sometimes SQL just isn’t enough. SQL is great for heavy lifting data preparation but certain data transformations are difficult in plain old SQL and its ability to summarise data is limited. This book is all about pandas, a Python library for data manipulation, plotting and basic data analysis.
The book is a comprehensive guide to the pandas library and will get you through the most awkward data manipulations you are likely to encounter.
Intermediate knowledge of Python is required.
This book is a fun, well written and comprehensive introduction to a wide range of common machine learning algorithms. The author takes you through building up each algorithm step by step and sets the context for why the algorithm does what it does. Intermediate knowledge of Python and programming is required to get the most benefit from this book. For a bonus challenge, work through the exercises using your pandas knowledge from Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython!