List of Data Science Books to Read

Here is the list of recommended data science books for reading:

1. Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo
2. Data Analytics: Master The Techniques For Data Science, Big Data And Data Analytics by Robert Keane
3. Practical Data Science with R by Nina Zumel
4. Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
5. Data Science For Dummies by Lillian Pierson
6. Big Data For Dummies by Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman
7. Python for Data Analysis: Data Wrangling With Pandas, NumPy and IPython,by Wes McKinney
8. Storytelling With Data: A Data Visualization Guide for Business Professionals, by Cole Nussbaumer Knaflic
9. Hadoop, the Definitive Guide: Storage and Analysis at an Internet Level, by Tom White
10. Doing Data Science: Straight Talk from the Frontline, by Cathy O’Neil and Rachel Schutt
11. Data Science from Scratch: First Principles with Python by Joel Grus
12. R Cookbook: Proven Recipes for Data Analysis, Statistics, and GFraphics (O’reilly Cookbooks) by Paul Teetor
13. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham
14. The Data Science Handbook by Field Cady
15. The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists by
    Carl Shan
16. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost
17. Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce
18. Learning R: A Step-by-Step Function Guide to Data Analysis by Richard Cotton
19. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press) by John D. Kelleher
20. Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark by Russell Jurney

Note: If you find some data science books which are missing in this list then please feel free to send us via email. Our email is asif@marktechpost.com.

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