Author: Jeffrey Leek
This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks.
Authors: Roger D. Peng and Elizabeth Matsui
This book writes down the process of data analysis with a minimum of technical detail. What they describe is not a specific “formula” for data analysis, but rather is a general process that can be applied in a variety of situations.
Author: Al Sweigart
In this book, you’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.
Author: Ben G Weber
This book is intended for practitioners that want to get hands-on with building data products across multiple cloud environments and develop skills for applied data science.
Authors: Shai Shalev-Shwartz and Shai Ben-David
This book includes a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
Authors: Blum, Hopcroft, and Kannan
This data science book is a great blend of lectures in the modern theoretical course in data science.
Contributors: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen
This tutorial aims to get you familiar with the main ideas of Unsupervised Feature Learning and Deep Learning.
Author: Jake VanderPlas
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.
Authors: Kareem Alkaseer
This book is a great source of learning the concepts of Machine Learning and Big Data.
Author: Allen B Downey
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.
Author: Allen B Downey
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.
Author: Reza Nasiri Mahalati
This compilation by Professor Sanjay emphasizes 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.
Author: Stephen Boyd and Lieven Vandenberghe
This book provides a comprehensive introduction to the subject and shows in detail how such problems can be solved numerically with great efficiency.
Author: Sean Luke
This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts.
Author: Hal Daumé III
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 some of the finest data science books that we recommend. Have something else in mind? Comment below with your list of some awesome data science books.
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