Nowadays, Data Science is one of the most popular and high-paid fields to make a career. And, to become a professional data scientist, you can read many amazing data science books and learn all the concepts in-depth.
That’s why, In this article, we’ve handpicked the top 12 best data science books for beginners who help you become knowledgeable in this field and grow your career.
Without any further delay, let’s begin.
📌 Table of Contents
What is Data Science?
Data Science is a field of study that deals with large volumes of data using modern tools, algorithms, and techniques to find meaningful information and unseen patterns from the raw data to make better business decisions & strategies.
Data Science uses machine learning algorithms to build predictive models. It is mainly used to analyze raw data and come up with meaningful insights and hidden patterns.
Can You Learn Data Science From Books?
Books are one of the easiest and smartest ways to learn any concept. With Data Science books, you can gain in-depth knowledge about computing, mathematics, probability, programming, machine learning, and many more. Experts in their field write these Books, which help you learn all the concepts without others’ perspectives, and you can learn more effectively.
Top 12 Best Data Science Books
Here are some of the best handpicked Data Science Books that help you learn about data science topics from scratch and become an expert.
1. Practical Statistics for Data Scientists
Originally Published: 2017
Author: Peter Bruce and Andrew Bruce
It’s a complete beginner-oriented data science book that includes all the major concepts of data science, including randomization, sampling, distribution and sample bias, etc. In this book, you’ll learn about each topic with the simplest examples.
If you want to explore all the concepts of data science without learning in-depth information, then you can consider this book to read and learn about data science. Mainly you’ll learn about statistics in this data science book.
2. Head First Statistics: A Brain-Friendly Guide
Originally Published: 2008
Author: Dawn Griffiths
It is one of the best data science books to start learning about data science simply. It’s a friendly and conversational book that explains all data science concepts, including correlation, regression, probability distributions, statistics, etc.
It includes data, charts, and many visual representations that help to understand the concept more friendly with puzzles, stories, quizzes, and real-world examples. From absolute beginners to professionals, anyone can read this book to learn all the data science concepts or revise the concepts effectively.
3. Python Data Science Handbook
Originally Published: 2016
Author: Jake VanderPlas
In this book, you’ll learn about the fundamentals of machine learning with Python requirements – IPython, numpy, pandas, seaborn, and scikit learn and some major data science concepts, including data mining and machine learning using Apache Spark, and important machine learning models.
This data science book is completely based on python basics and is helpful for those from a Programming background who want to know more about python effectively. It’s one of the highly-recommended books for scientific computing in Python.
4. Think Stats: Exploratory Data Analysis, Second Edition
Originally Published: 2014
Author: Allen B. Downey
Think Stats can help you understand many amazing data science concepts, including probability, statistics for Python, correlation, hypothesis testing regression, time series analysis, distributions, visualization, and analytical methods. You can learn all the data science concepts most simply with practical examples.
If you want to learn about using tools of probability and statistics, then you’ll get complete knowledge about how to perform statistical analysis computationally in Python. It’s a practical handbook to learn the concept of data science with Python.
5. Introduction to Machine Learning with Python: A Guide for Data Scientists
Originally Published: 2016
Author: Andreas C. Mūller & Sarah Guido
If you’re an absolute beginner and want to start your journey in Data Science with Python, then you can surely consider this book to read and learn all the data science concepts with great examples. With this book, you’ll explore the fundamental concepts of machine learning, workflow, data preprocessing, algorithms, analyzing, implementation of algorithms, and machine learning models.
It’s a practical handbook for Python programmers to know more in-depth about machine learning by implementing machine learning algorithms.
6. Python for Data Analysis
Originally Published: 2017
Author: Wes McKinney
Python for Data Analysis is one of the most popular books to learn about Python for data analysis. In this book, you’ll learn about manipulation, process, cleaning, and efficiently crunching data in Python using popular libraries like Numpy and Pandas.
With this book, you can learn how to analyze data using Python most effectively and gain advanced insights into Pandas & Numpy. This book can save you many hours by knowing the right information in easy explanation.
7. Naked Statistics – Stripping the Dread from the Data
Originally Published: 2014
Author: Charles Wheelan
It’s a New York Times best-seller book showing statistics’ true meaning. In this book, you’ll learn all the statistics concepts, from basic to advanced, with real-world examples. It’s a slim, entertaining, and friendly book that helps you know everything about statistics, including descriptive statistics, correlation, probability, The Monty Hall Problem, central theorem, inference, polling, regression analysis, and machine learning.
It’s mainly focused on calculation and math, not computational. To become a data scientist, you can surely prefer this book to gain knowledge about detailed statistics.
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8. R for Data Science
Originally Published: 2017
Author: Hadley Wickham & Garret Grolemund
R is a popular programming language for data science applications. It’s used to analyze raw data and turn it into meaningful insights, knowledge, and understanding. This book will help you learn about the R programming language and manage data with different tools.
In this book, you’ll learn about Wrangle, Programming, Explore, Models, and Communicate. If you want to learn R programming languages, then this book will be helpful for you to learn all the concepts in the simplest way.
9. Deep Learning (Adaptive Computation and Machine Learning series)
Originally Published: 2016
Author: Ian Goodfellow, Yoshua Bengio & Aaron Courville
If you’re interested in deep learning, this book can give you all the details you require about deep learning, including linear algebra, probability theory, numerical computation, machine learning, mathematical and conceptual background, regularization, optimization algorithms, sequence modeling, and practical methodology.
This book explains these concepts with real-world examples so you can understand them more effectively. If you’re planning to make your career in deep learning, then you must read this book to clear your fundamentals and know all the concepts about deep learning.
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10. Data Science from Scratch
Originally Published: 2019
Author: Joel Grus
This book has the power to make you a master of data science. In this book, you’ll learn about all the major concepts from scratch of data science, including data science libraries, frameworks, modules, toolkits, and algorithms.
It’s not a simple book to read, and you can consider this book as a crash course to learn about data science with Python and some necessary topics like linear algebra, statistics, probability, hypothesis, gradient descent, getting data, working with data, regression, clustering and machine learning.
You can explore more in this book and level up your knowledge about data science.
11. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
Originally Published: 2019
Author: Aurelian Geron
If you already know the fundamentals of data science, then this is the 2nd data science book that is highly recommended to you. In this book, you’ll learn more about machine learning theory and its usage behind algorithms.
This book comes with amazing examples, minimal theory, Python frameworks like Scikit-Learn, and TensorFlow, and learn techniques for scaling deep neural nets. It’s really helpful for working on projects to understand the concepts more easily and take them as references. Once you’ve read the book, you’ll know all the concepts and functions behind models.
📌 Related article: Top companies of bangalore offering data science services
12. Understanding Machine Learning: From Theory to Algorithms
Originally Published: 2014
Author: Shai Ben-David & Shai Shalev-Shwartz
To learn about machine learning in-depth with all major concepts, this book can be helpful for you. In this book, you’ll learn from scratch about machine learning fundamentals, algorithms, neural networks, structured output learning, compression, and broad ideas about the view of machine learning.
For beginners, it’s a useful book where you can learn the theory and practical methods of machine learning in the most effective manner. It starts with fundamental machine learning concepts, and later you’ll learn about machine learning algorithms and advanced methods in the simplest way.
Final Words
Data Science is one of the in-demand and best fields to make a career. If you’re serious about becoming a data scientist, then surely a data science book can help you become an expert in data science and open new opportunities for your career.
There are hundreds of data science books available, but we’ve handpicked the top 12 data science books to help you start your journey in Data Science and make your career.
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