Top Data Science books recommended by experts

At MentorCruise, we are all about making the most out of the experience of others. As part of that, we've connected and asked dozens of experts and professionals about their favourite Data Science books – and here are the answers.

Table of Contents

Fundamentals of Data Science

Understanding the concepts of Data Science starts with understanding the fundamentals. On your way to mastery, it's crucial for you to understand how certain concepts were derived, and why things work like they do. Starting with these resources is the best way to do so.

Python Data Science Handbook: Essential Tools for Working with Data

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.<…

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How to Become a Data Scientist: A Guide for Established Professionals

Any experienced professional hoping to break into data needs this book because it strikes the right balance between strategic advice and pragmatic tips you can start applying today.
This book gives experienced professionals not just the tactical tips they need to break into a Data Science career, but something even more important - the confidence to make the leap! - Nick Singh, Author of Ac…

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Machine Learning: An Artificial Intelligence Approach

The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn ing processes is of great significance to fields concerned with understanding in telligence.

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Think Stats

Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health.

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The Kaggle Book: Data analysis and machine learning for competitive data science

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career.
The first book of its kind, The Kaggle Book assembles in o…

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Probability: For the Enthusiastic Beginner

This book is written for high school and college students learning about probability for the first time. It will appeal to the reader who has a healthy level of enthusiasm for understanding how and why the various results of probability come about.

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Additional Data Science Reading

These books are not required for you to learn Data Science, but they are highly recommended for you to deepen your knowledge.

Data Science from Scratch: First Principles with Python

To really learn data science, you should not only master the tools―data science libraries, frameworks, modules, and toolkits―but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some pro…

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Introduction to Probability

Probability is simply how likely something is to happen. Whenever we're unsure about the outcome of an event, we can talk about the probabilities of certain outcomes—how likely they are. The analysis of events governed by probability is called statistics.

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Grokking Deep Learning

Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.

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Statistics for Data Scientists

Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse…

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Speech and Language Processing

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA cour…

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Specializations and Deeper Data Science Knowledge

You've got your basics in order – time to move on to some advanced and specialized concepts. Data Science is evolving every day, these books can help you master it.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society.

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