Best Data Science Books

from Beginners to Advanced in 2023

Image Credit: Analytics Drift

Beginner Books


Data Science for Dummies by Lillian Pierson

This classic beginner-friendly book briefly introduces the tools and concepts of data science, covering the basics of data analysis, data visualization, and Python.

Python For Data Analysis by Wes McKinney 

Focusing on data analysis with Python, this book is ideal for beginners. It strongly emphasizes utilizing the Pandas library with practical examples and exercises.

Data Science For Business by Foster Provost and Tom Fawcett

This is a unique book in this data science books list. It offers non-technical explanations of key ideas for anyone interested in data science in the business world.

Intermediate Books


Python Machine Learning by Sebastian Raschka and Vahid Mirjalili

Readers with some basic science skills will benefit from this book. It uses Python to study different machine-learning techniques and gives practical examples with useful insights.

Practical Statistics for Data Scientists by Andrew Bruce and Peter Bruce

As the name suggests, this book focuses on statistics and actually bridges the gap between data science and statistics for intermediate learners.

Python Data Science Handbook by Jake VanderPlas

Filled with practical knowledge and helpful advice on Python-based data science tools. This book is great for readers who want to improve their data analysis knowledge and use Jupyter Notebook.

Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

A technical book that discusses machine learning and statistical learning methods. Based on R programming language, it is suggested for people with a solid background in statistics.

Advanced Books


Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This is a comprehensive guide that explores deep learning techniques. It is a good read for advanced students who wish to explore neural networks and advanced machine learning concepts.

Pattern Recognition and Machine Learning by Christopher M. Bishop

Written by Christopher M. Bishop, this book is recommended for readers familiar with complex mathematical ideas.

Information Theory, Inference, and Learning Algorithms by David MacKay

This unique data science book combines three complex topics: information theory, inference, and learning algorithms.

Join our WhatsApp Group Now!

Get the latest updates on AI developments.

Produced by: Jalaj Jain Designed by: Prathamesh