Recommended Books for Aspiring Data Scientists

Books help in holistic learning. With this thought in mind, we have curated some of the best books for aspiring data scientists that cover the essentials of data science, math, statistics, programming, etc.

The profile of data scientists is not just the most attractive job profile these days, it’s highly challenging too. To start a career in data science, you need to work on your elementary skills in mathematics, statistics, computer science, programming data analysis, etc. What is better than books when it comes to learning new concepts? We recommend you go through these books for aspiring data scientists and boost your learning in the most effective way.

Books for Aspiring Data Scientists

1. Essential Math for Data Science by O’Reilly
2. Algorithms to Live by: The Computer Science of Human Decisions by Brian Christian and Thomas L. Griffiths
3. Think Stats by O’Reilly
4. Data Science from Scratch: Principles with Python by O’Reilly
5. The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists by Carl Shan, William Chen, Henry Wang, and Max Song
6. The Art of Data Science by Roger D. Peng and Elizabeth Matsui
7. Python for Data Analysis by O’Reilly
8. Data Science For Dummies by Lillian Pierson
9. Doing Data Science: Straight Talk from the Frontline by O’Reilly
10. Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo

Let’s begin!

1. Essential Math for Data Science by O’Reilly  

This book by O’Reilly is among the best Math books on data science. You can explore the practical examples with Python code to understand how the math applies in the real world. It covers topics like probability, statistics, hypothesis testing, linear algebra, calculus, as well as machine learning. You will master the concepts of statistics and hypothesis testing and perform calculus derivatives and integrals completely from scratch in Python.

2. Algorithms to Live by: The Computer Science of Human Decisions by Brian Christian and Thomas L. Griffiths

Algorithms to Live By has been named as the must-read brain books by Forbes. Brian Christian and Tom Griffiths are the authors of this book. They introduce you to some actual computer science concepts along with some cognitive science and philosophy concepts like game theory, trolley problem, secretary problem, etc. You will also learn how algorithms play an important role in our lives, starting from remembering things to making small and big decisions. 

3. Think Stats by O’Reilly

Think Stats: Probability and Statistics for Programmers is a textbook for a new kind of introductory prob-stat class. It covers statistics techniques for exploring real data sets. It uses a computation approach where students can write programs in Python to develop and test their understanding.

4. Data Science from Scratch: Principles with Python by O’Reilly

In this book by Joel Grus, you can learn about data science libraries, frameworks, modules, and toolkits. You will also learn to implement fundamental data science tools and algorithms from scratch. Before reading this book, we recommend some prior knowledge or experience using Python. 

Data Science from Scratch is targeted towards intermediate programmers willing to learn data science, along with  other data science aspirants. 

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5. The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists by Carl Shan, William Chen, Henry Wang, and Max Song

This book by authors – Carl Shan, William Chen, Henry Wang, and Max Song is among the best data science books coming directly from the people working with data science. This book contains interviews with the leading data scientists who have been creating their own programs. 

The book offers a different perspective of working with data science and helps beginners learn from the mistakes these data scientists committed, how they grew in their career, what type of strategies worked for them, etc. Consider this book if you look forward to hearing practical career advice and insights into data science.

6. The Art of Data Science by Roger D. Peng and Elizabeth Matsui

The Art of Data Science is a data science book that explores and finds discoveries within any data lake. It focuses on data analysis and cleaning to dig in the underlying information. Since both the authors are data scientists, they share their experiences to help both beginners and experienced learn data science and analysis and produce successful results in data science projects.

7. Python for Data Analysis by O’Reilly

This book by Wes McKinney will help you learn how to manipulate, process, clean, and crunch data in Python. It’s perfect if you already have some experience working with data and programming with Python. You will learn the basic and advanced NumPy features, data analysis tools in the pandas library, and create scatter plots and static or interactive visualizations with matplotlib. The book covers detailed examples of solving problems in web analytics, social sciences, finance, and economics.

8. Data Science For Dummies by Lillian Pierson

Lillian Pierson is the author of this book. It will teach you the simple concepts of Data Science like data engineering, machine learning, MPP platforms, Spark, NoSQL, Hadoop, big data analytics, MapReduce, and artificial intelligence. You can also understand the business aspect of data science. In other words, “Data Science for Dummies” is an excellent guide if you are looking forward to working as a data scientist. 

The author also talks about the different ways data science can happen in everyday life. If you are new to Data Science and want to know its basic principles, this book is for you.

9. Doing Data Science: Straight Talk from the Frontline by O’Reilly  

Cathy O’Neil and Rachel Schutt based this book on Columbia University’s Introduction to Data Science class. In other words, this is a very good book for beginners or those who are switching their careers to become a Data Scientist.

O’Neil is a data science consultant, collaborating with Schutt, the class instructor, to bring the course to the general public. They have covered the concepts of Statistical inference, exploratory data analysis, algorithms, data wrangling, Logistic regression, Financial modeling, Data visualization, Data engineering, MapReduce, Pregel, and Hadoop.

In the book, in addition to establishing basic knowledge about data science, they share relevant case studies, something that makes readers understand the theory in a better way. 

10. Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo

Numsense! Data Science for the Layman: No Math Added” focuses on the mathematical side of data science and helps the reader simplify certain algorithms so that they are less cumbersome and easier to understand. In addition to providing actual examples, the authors spend each chapter explaining each of the useful algorithms specifically. They have also covered the advantages and disadvantages of each of the algorithms. It is one of the must have books for aspiring data scientists!

Wrapping Up

Now that you know which books for aspiring data scientists you should be reading, it’s highly recommended that you take up a relevant data science course. Learning is the key to growth and no matter if you are starting a career or are an expert, you would always need to keep sharpening your skills further.

Keep Learning!

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