Book Suggestions for Data Practitioners
This is a collection of books that I've either read or partially read that I think is valuable for people who work in Data.
These books are for a range of skill levels from beginners to advanced data folks.
A lot of these books receive continued updates, so be sure to get the newest version.
These 2 don't really fit in any other section, but are probably the most useful.
- How to Lead in Data Science by Jike Chong and Yue Cathy Chang
- Useful for any leader in Data
- Build a Career in Data Science by Emily Robinson and Jacqueline Nolis
- These books by Tufte are really good for teaching the theory of data visualizations as well as exploring the wide range of what is possible in data visualizations.
- Working Effectively with Legacy Code by Michael Feathers
- Refactoring: Improving the Design of Existing Code by Martin Fowler, with Kent Beck
- This is the most boring book I have ever read. It systematically goes through all possible code changes that can be made to object oriented code. It definitely made me a significantly better programmer.
- Designing Data-Intensive Applications by Martin Kleppmann
- Recommender Systems by Charu C. Aggarwal
- Python for Data Analysis by Wes McKinney
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- Feature Engineering for Machine Learning by Alice Zheng, Amanda Casari
Statistics and Analytics
- Trustworthy Online Controlled Experiments by Ron Kohavi, Diane Tang, Ya Xu
- The Cartoon Guide to Statistics by Larry Gonick coauthored with Woollcott Smith
- Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference by Cameron Davidson-Pilon
- Design and Analysis of Experiments by Douglas C. Montgomery
- Statistical Inference by George Casella, Roger L. Berger