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. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost
  16. Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce
  17. Learning R: A Step-by-Step Function Guide to Data Analysis by Richard Cotton
  18. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press) by John D. Kelleher
  19. 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

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