Earlier, we came up with a list of some of the best Machine Learning books you should consider going through. In this article, we have come up with yet another list of the recommended books for Data Science.
Written by Blum, Hopcroft and Kannan, this book for data science is a great blend of lectures in the modern theoretical course in data science.
This tutorial aims to get you familiar with the main ideas of Unsupervised Feature Learning and Deep Learning.
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.
This book by Kareem Alkaseer is a great source of learning the concepts of Machine Learning and Big Data.
Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. This one of the most recommended books for data science.
Think Bayes is an introduction to Bayesian statistics using computational methods. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics.
This compilation by Professor Sanjay emphasises on applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Link to previous years’ course notes by professor Boyd can be found here.
This book provides a comprehensive introduction to the subject and shows in detail how such problems can be solved numerically with great efficiency.
This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts.
CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).
These are the books for data science we highly recommend. If we missed out something, let us know. Comment below and share your list of favorite books for data science.