This is a list of must-read books on Data Science which I have collected from various sources. I am not sure how much they are ‘Recommended or Must-Read’. But hope it helps to measure the progress of my reading on Data Science/Mining. I am going to continue to edit this list.

This list can be also called ‘my reading challenge list’. Yes I am about to begin a challenge to read 100 books on data science. The thoughts and reasons behind it may be shared later in another post.

Enjoy reading!

1. Data Mining: Concepts and Techniques second Ed.
2. Python for Data Analysis by Wes mcKinney
3. Data Smart by John W. Foreman
4. Doing Data Science by Cathy O’Neil & Rachel Schutt
5. Data Science for Business by Foster Provost & Tom Fawcett
6. Naked Statistics: Stripping the Dread from the Data
7. Data Science for Business: What you need to know about Data Mining and Data-Analytic Thinking
8. Data Smart: Using Data Science to Transform Information into insight
9. Data Science from Scratch: First Principles with Python
10. A Cookbook: Proven Recipes for Data Analysis, Statistics, and GFraphics
11. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
12. Nonsense! Data Science for the Layman: No Math added
13. Big Data for Business: Your comprehensive Guide to Understand Data Science, Data Analytics and Data Mining to Boost More Growth and Improve Business (Data Analytics Book Series) (volume 2)
14. Practical Data Science with R
15. Introduction to Machine Learning with Python: A Guide for Data Scientis
16. The Data Science Handbook
17. Doing Data Science: Straight Talk from the Frontline
18. Data Science for Dummies
19. Python Data Science Handbook: Essential Tools for Working with Data
20. The Data Science Handbook: Advise and Insights from 25 Amazing Data Scientists
21. Data Analytics: Master The Techniques For Data Science, Big Data and Data Analytics
22. Practical Statistics for Data Scientists: 50 Essential Concepts
23. Data Analytics Made Accessible: 2017 Edition
24. Learning R: A Step-by-Step Function Guide to Data Analtysis
25. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)
26. Big Data A Revolution That Will Transform How We Live, Work and Think
27. Automate This: How Algorithms Came to Rule Our World
28. The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t
29. Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
30. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
31. Privacy in the Age of Big Data: Recognizing Threats, Defending Your Rights, and Protecting Your Family
32. R Cookbook (Paul Teetor)
33. Machine Learning for Hackers
34. R Graphics Cookbook
35. Programming Collective Intelligence: Building Smart Web 2.0 Application
36. Python for Data Analysis: Data Wrangling With Pandas, NumPy, and IPython
37. Agile Data Science: Building Data Analytics Applications with Hadoop
38. The Visual Display of Quantitative Information
39. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
40. Beautiful Data: The Stories Behind Elegant Data Solutions
41. Data Mining: Practical Machine Learning Tools and Techniques
42. Visualize This
43. Natural Language Processing with Python
44. Business Intelligence Roadmap – The Complete Project Lifecycle for Decision-Support Applications, Larissa T. Moss and Shaku Atre
45. Data Warehousing Concepts and Strategies, Stefan M. Neikes, Sumit Sircar and Bijoy Bordoloi
46. The Data Warehouse Life Cycle by KIMBALL
47. Data Warehouse Project Management by Adelman & Moss
48. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling by Kimball
49. Building the Data Warehouse by Bill Inmon