Data Science Books for Beginners and Experts: Top Picks

This is a great book for learning about probability as it gives clear explanations that mirror real-life situations. It delves into a wide range of applications and case studies.

1. Introduction to Probability

The book will show you practical techniques for developing your own machine-learning solutions using Python. Learn to build an ML application using Python and Scikit-Learn library.

2. Introduction to Machine Learning with Python

Featuring a realistic, up-to-date introduction to Python data science tools, this book explains how to manipulate, analyze, clean, and crunch datasets using Python.

3. Python For Data Analysis

It introduces approximate inference methods for quick approximate answers when exact solutions aren't possible. Graphical models are used to characterize probability distributions.

4. Pattern Recognition and Machine Learning

This book teaches you how exploratory data analysis is one of the first steps in data science, as well as how random sampling can eliminate bias and produce better datasets.

5. Practical Statistics for Data Scientists

A textbook that discusses linear algebra, probability, information theory, numerical computations, ML, and more. Deep learning, recurrent neural networks, etc., are also covered.

6. Deep Learning

The book illustrates how to mine data that arrive too quickly for exhaustive processing using locality-sensitive hashing and stream-processing methods.

7. Mining of Massive Datasets

A complete guide with a sample resume  for  Power BI Developers.