Here is the list of recommended data science books for reading:
- Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo
- Data Analytics: Master The Techniques For Data Science, Big Data And Data Analytics by Robert Keane
- Practical Data Science with R by Nina Zumel
- Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
- Data Science For Dummies by Lillian Pierson
- Big Data For Dummies by Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman
- Python for Data Analysis: Data Wrangling With Pandas, NumPy and IPython,by Wes McKinney
- Storytelling With Data: A Data Visualization Guide for Business Professionals, by Cole Nussbaumer Knaflic
- Hadoop, the Definitive Guide: Storage and Analysis at an Internet Level, by Tom White
- Doing Data Science: Straight Talk from the Frontline, by Cathy O’Neil and Rachel Schutt
- Data Science from Scratch: First Principles with Python by Joel Grus
- R Cookbook: Proven Recipes for Data Analysis, Statistics, and GFraphics (O’reilly Cookbooks) by Paul Teetor
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham
- The Data Science Handbook by Field Cady
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce
- Learning R: A Step-by-Step Function Guide to Data Analysis by Richard Cotton
- Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press) by John D. Kelleher
- 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.
