This article covers some of the most popular books on Data Science and is to assist newcomers in exploring the world of data science and experienced practitioners to get deeper knowledge. Below is a list of the top 8 that I have found useful or been recommended.
The following books will give you knowledge and understanding of important areas of data science such as Statistics, Data Science, Machine Learning, Deep Learning and Deployment.
Introductory Level
1. An Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
This book is very beginner-friendly, easy to read, with a lot of illustrations and real-world scenarios which it combines for an easy to comprehend machine-learning lesson.
Each chapter of this book has a great tutorial on implementing different modules for analysis and modelling in R.
2. Practical Statistics for Data Scientists
by Peter Bruce
This is a comprehensive reference guide for many of the concepts in statistics for data science. It’s a good book to bridge the gap between statistics and data science. Although the book assumes familiarity with R, it’s still a good book to learn statistical concepts for Python programmers.
3. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
This book introduces** big data and covering data analytics lifecycle**. Its Easy-to-read and it clears all the concepts which otherwise one may struggle to find elsewhere. Advanced analytics using MapReduce, Hadoop, and SQL are also introduced to the reader.
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