Did you know that Harvard Business Review called data science the sexiest job of the 21st century? And Python is the most popular language used in data science… so where are all the Python books for data science?
We’ve got them right here. Plus we included a data science fundamentals bonus book that you don’t want to miss.
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TLDR: Top Python Books for Data Science in 2021
We picked the absolute best Python books for data science based on the following criteria:
🔥 Best Overall 🔥
Intro to Python for Computer Science and Data Science
💥 Best for Newbies 💥
Data Science from Scratch: First Principles with Python
💸 Best Value 💸
Python for Data Science: The Ultimate Beginners’ Guide
Top Python Books for Data Science
Data Science Using Python and R by Chantal LaRose and Daniel LaRose is for readers who have no programming or analytics experience, so it’s great for beginners.
You’ll start off by learning about Python and R. Then you’ll move onto step-by-step walkthroughs to solve data science problems.
Some of the topics covered include:
- data preparation
- exploratory data analysis
- decision trees
- naive Bayes classification
- neural networks
- random forests
And much, much more.
There are over 500 exercises throughout Data Science using Python and R. With these hands-on challenges, you’ll solve business problems using real-world data.
In addition, you’ll learn how to enhance profitability with data-driven error costs.
You have to know math to be a data scientist.
And Data Science and Machine Learning by Dirk Kroese, et al. is devoted entirely to mathematics.
This textbook is intended to give comprehensive insight into the mathematics and statistics required for data science.
Some of what you’ll learn includes:
- visualizing data
- statistical data
- Monte Carlo methods
- deep learning
- linear algebra
- probability and statistics
In addition, there’s a Python primer which includes data science libraries like matplotlib and scikit-learn.
Data Science and Machine Learning is an exercise-heavy book with step-by-step walkthroughs of solutions. Algorithms are presented in Python.
Want to take a course to brush up on your data science math skills? Sign up for Data Science Math Skills on Coursera.
Learn more about the data science career field and skill requirements in RTC’s interview with a working data scientist:
Intro to Python for Computer Science and Data Science by Paul Deitel and Harvey Deitel contains a unique teaching model as it applies to both aspiring developers and data scientists.
You’ll learn to program using artificial intelligence, Big Data and the Cloud… All in Python, of course.
In addition, you’ll learn about Jupyter Notebooks, an open-source web application used for data cleaning.
Then you’ll work on hundreds of hands-on exercises and case studies to reinforce the fundamentals of Python in data science.
So instead of just sitting back and reading, you’ll be working through actual coding problems.
Intro to Python is ideal to use in conjunction with other data science materials such as books or courses.
🔥 Geena’s Hot Take
Don’t get me wrong: there are plenty of awesome Python books for data science in this list. But Intro to Python for Computer Science and Data Science is the best.
You’ll see 5-star review after 5-star review singing its praises.
No other book teaches Python for both computer science and data science. And it does so in a detailed, compelling way.
If there’s one book I think you should buy on this list, it’s Intro to Python for Computer Science and Data Science.
Python Data Science Handbook by Jake VanderPlas is a handy reference book for experienced data scientists.
For example, you’ll work with various data science tools such as:
This is unique because most resources usually only cover one or a few of these tools. Rarely will you see so many examined in one book.
It’s also a useful reference for:
- manipulating and cleaning data
- data visualization
- building statistical models
- building machine learning models
If you’re a data scientist in need of some good reference materials, Python Data Science Handbook is one of the best Python books for data science.
Data Science for Beginners by Andrew Park is actually a compilation of four books in one. It covers:
- Python programming
- data analysis
- machine learning
- data science using Python
You’ll learn about concepts and methods to build your foundation on these topics. In addition, you’ll work with real-world examples.
But first, you’ll learn Python from scratch. So you’ll learn how to install it, basic operations and more.
Then you’ll build on the fundamentals of Python by learning about object-oriented programming (OOP), inheritance and polymorphism.
After that, you’ll learn 5 steps to use data analysis to your advantage when looking at data. Also, you’ll examine how companies improve their businesses by utilizing data analysis and data science.
Data Science for Beginners is primarily geared towards software engineers and project managers looking to take their skills to the next data-driven level.
Never programmed before? Take the course Data Science for Non-Programmers on Educative.io.
Data Science with Python and Dask by Jesse Daniel heavily focuses on the analytics tool Dask. Because of Dask, you’ll be able to build scalable projects that are able to handle huge datasets.
Dask is a library that complements other Python libraries like pandas, NumPy and scikit-learn.
Throughout the book, you’ll work on some fun real-world projects:
- analyze NYC Parking Ticket data
- create machine learning models
- build interactive visualizations
- build clusters
In addition, you’ll learn how to implement your own algorithms… And work with structured and unstructured datasets.
Finally, you’ll learn how to package and deploy Dask applications.
You should have experience with Python and PyStack before reading Data Science with Python and Dask.
While statistical books are plentiful, you’ll rarely see them as they relate to data science.
Practical Statistics for Data Scientists by Peter Bruce, et al. uses Python and R to teach over 50 essential statistics concepts. In addition, you’ll also learn how to apply statistical methods to data science.
With over 300 pages, you’ll look at:
- exploratory data analysis
- data and sampling distributions
- statistical experiments
- regression and prediction
With example-heavy text, you’ll learn about things such as how random sampling can reduce bias and using regression to estimate outcomes.
In addition, you’ll learn about using unsupervised learning to extract meaning from unlabeled data and how to use experimental design.
You should have experience with Python before reading Practical Statistics for Data Scientists.
We consider Data Science from Scratch by Joel Grus to be one of the best Python books for data science.
First of all, you’ll get a crash course in Python. Also, you’ll learn how to implement algorithms from scratch.
In addition, you’ll learn about key Python libraries, toolkits and frameworks. Plus you’ll learn the principles behind them.
After learning about Python, you’ll acquire the basics of:
- linear algebra
Then Data Science from Scratch touches on machine learning.
Finally, you’ll explore how to implement models such as neural networks and decision trees. Plus you’ll discover recommender systems and natural language processing (NLP).
Looking for a course to take alongside this book? Try out Introduction to Data Science with Python on DataCamp.
Data Science Projects with Python by Stephen Klosterman gives us aspiring data scientists something we desperately need: practice.
You’ll start by installing packages to set up your data science coding environment. Then you’ll load data into a Jupyter Notebook.
After that, you’ll use matplotlib to create data visualizations… Plus much more.
The structure of the book will allow you to:
- identify problems
- solve problems
- use visualizations to illustrate data
By the end of this book, you should be confident in using machine learning algorithms to perform data analysis.
Data Science Projects with Python is for readers already familiar with Python and data analytics. It will also be helpful to be comfortable with algebra and statistics.
Python for Data Science: The Beginners’ Guide by Ethan Williams is geared towards developers wanting to learn Python as it applies to data science. You do not need any previous programming experience.
There are 4 main chapters in the book:
- Python Basics
- Data Analysis with Python
- Data Visualization with Python
- Machine Learning with Python
You’ll be introduced to a few Python libraries such as NumPy, pandas and Seaborn.
Throughout the book, you’ll see examples with real-world applications. Also, there are plenty of exercises to work on.
In addition to in-book exercises, there are tons of links to external practices and readings.
Really want to get data science down? Sign up for the Zero to Mastery course Complete Machine Learning and Data Science.
Python Data Science by Andrew Park is a short yet highly informative book.
First and foremost, you’ll learn about the proper steps to take and algorithms to use to help you sort through data.
Then you’ll learn how to implement Python within data science while exploring functions and modules.
Python Data Science also exhibits 7 of the most important algorithms in data science alongside 9 data mining techniques.
In addition to collecting and cleaning data, you’ll also learn how to perform analysis to extract vital information.
You should have experience with Python before reading Python Data Science.
We think Doing Data Science by Cathy O’Neil and Rachel Schutt is an excellent introductory book to data science. So while there is not a focus on Python, you can get a lot out of learning the fundamentals of data science.
You’ll learn about:
- statistical inference
- logistic regression
- data visualization
- data journalism
- Hadoop, MapReduce
And much more.
In these chapter-long lectures, you’ll gain insight from data scientists that have worked at FAANG-level companies. This includes case studies.
You should be familiar with algebra, statistics, and programming.
Ready to interview for that data science job? Sign up for Interviewing for Data Science on Coursera.
Python Books for Data Science: Conclusion
Today we looked at the best Python books for data science and came up with our favorites:
Best for Newbies
Data Science from Scratch: First Principles with Python
So regardless of your needs, we think there’s a Python book for data science that will work for you.
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- 4 Best Data Science Courses of 2021 [Educative, DataCamp, Zero to Mastery, Codecademy]
- DataCamp or Codecademy Pro [Best Place to Learn Data Science?]
- Is Doing Data Science a good book for beginners?
We think Doing Data Science is an excellent introductory book to data science. So while there is not a focus on Python, you can get a lot out of learning the fundamentals of data science. With over 350 pages, you'll learn about
statistical inference, algorithms, logistic regression, data visualization
data journalism, Hadoop, MapReduce, and much more. In these chapter-long lectures, you'll gain insight from data scientists that have worked at FAANG-level companies. This includes case studies. You should be familiar with algebra, statistics, and programming.
- What are the best Python books for data science?
We picked the best Python books for data science based on the following criteria. For best overall, our top choice is Intro to Python for Computer Science and Data Science. For newbies, we think Data Science from Scratch: First Principles with Python is a good fit. And for best value, we think Python for Data Science: The Ultimate Beginners' Guide is the way to go. So regardless of your needs, we thinks there's a Python book for data science that will work for you.
- Is Python Data Science Handbook any good?
We think Python Data Science Handbook is a handy reference book for experienced data scientists. For example, you'll work with various data science tools such as IPython, NumPy, pandas, Matplotlib and beyond. This is unique because most resources usually only cover one or a few of these tools. Rarely will you see so many examined in one book. It's also a useful reference for: manipulating and cleaning data, data visualization, building statistical models, building machine learning models and more. If you're a data scientist in need of some good reference materials, Python Data Science Handbook is one of the best Python books for data science.