Data Science is definitely one of the hottest market right now. Almost every company has a data science positions opened or is thinking about one. That means it’s the best time to become a Data Scientist or hone your skills if you’re already one and want to level up to more senior positions. This text covers some of the most popular books on Data Science.

If you’re looking for courses to learn Data Science effectively, check out our recommendations for the Best Data Science Courses on Coursera.

Note: remember that reading books or following courses online is not enough to become a Data Scientist. You need to actively practice Python and Data Science methods by writing code.

Introductory Level Data Science Books



If you’re just starting your adventure with Data Science, you should definitely try this book:



Data Science from Scratch is what the name suggest: an introduction to Data Science for total beginners.

You don’t even have to know Python to start.

You’ll get a crash course in Python and Data Science, learn linear algebra and statistics, and will be able to analyse data thanks to this book.



If you’re a total beginner but you’d like to go more in Machine Learning direction from, Introduction to Machine Learning with Python is a book for you.

It also doesn’t assume you know Python.

With this book you will learn important machine learning algorithms and implement them from scratch in Python.

Data Science Job

Finally, if you want to have an overview of what it means to be a Data Scientist, then have a look at a concise book Data Science Job: How to become a Data Scientist which will guide you through the process.

You will learn what skills you need to acquire to become a data scientist, how data scientist works or how to find your first job in data science. There’s plenty of valuable materials around becoming a data scientist.

Intermediate Level Data Science Books



If you’ve already read 1 or 2 Data Science books, did 1 or 2 projects for yourself and get accustomed to working with data a little bit, here are the books which will take you further.

To progress past the junior data scientist level the key is practice: code as much as possible to stay on top.



Python for Data Analysis is the perfect way to get to know better standard Python libraries like NumPy or pandas. It is a complete treatise starting also from reminding you how Python works.



Use NumPy and pandas to analyse and visualise data coming from real world examples. This book explores how to extract useful information from any data you might encounter as a data scientist or a data analyst.

Python Data Science Handbook is a great guide through all standard Python libraries as well: NumPy, pandas, Matplotlib, Scikit-learn.



This book is a great reference for any data related problems you might have as a data scientist. Clean, transform and manipulate data to extract actionable insights.

Python Machine Learning is somewhere between intermediate and expert. It will appeal both to experts and people who are somewhere in the middle.

It starts gently and then proceeds to most recent advances in machine learning and deep learning.

Great read for any machine learning engineer or a data scientist experimenting with machine learning algorithms!

Hands-On Machine Learning with Scikit-Learn and TensorFlow (2nd edition is out!) is an amazing reference for a mid-level data scientist.

This book covers all fundamentals (classification methods, dimensionality reduction) and then gets into neural networks and deep learning using Tensorflow and Keras to build machine learning models.



Python for Finance is a must-read if you’re into finance and data science. It focuses on how to use data science tools to analyze financial markets and have many great examples illustrating that. It’s very practical and will also appeal to people who don’t work in finance on a daily basis.

The book develops a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study.

Expert Level Data Science Books



If you’re approaching the expert level, then actually reading scientific papers often makes more sense than reading books. However it is also time you study and implement deep learning in your solutions to go beyond the classical statistics.

Don’t forget that at this stage you also should know a bit about DevOps and software engineering side of machine learning: Dockers, Kubernetes and all that goes with them.

To start with two fantastic references are:



Deep Learning with Python was written by a creator of Keras, one of the most popular machine learning libraries in Python.

The book starts gently, is very practical, gives pieces of code you can use right away and has in general many useful tips on using deep learning.

An absolute must read in deep learning.



Deep Learning is an amazing reference for deep learning algorithms.

It doesn’t contain much code, but has great insights about how one should approach problems with machine learning: written by pioneers of deep learning.

This book is a definite guide to how to think about machine learning algorithms and how to improve upon them in specific situations.



If you’re into mathematics, then you’ll love Machine Learning: a Probabilistic Perspective.

It’s a tour-de-force through mathematics behind all machine learning methods.

You probably won’t be able to read it at once, but it’s very useful as a reference in machine learning research.



Learning Data Science beyond books



That’s all for the list of the best Data Science books! If you’d like to check out these books for free, then try Audible – they offer a free

And don’t forget about our book Data Science Job which you can find here or here (our website for the book). We believe it’s a great introduction into data science for beginners with explanation of what data scientists really do.

If you want to keep on learning, then have a look at other resources we collect on Data Science Rush. We are especially passionate about courses available on Coursera which give you a great opportunity to learn all the fundamentals in data science.

Coursera has on offer a classical machine learning course from Andrew Ng from Stanford, IBM Data Science Professional Certificate, Google IT Automation course and much more.

To have a more detailed look at these courses, have a look at these lists:

On the other hand if you’re looking for a laptop for your data science experiments, check out this list.

All in all, data science is about practicality. So don’t forget to code yourself into data science.

Data Science Community

Join our community on Facebook to ask any questions about Data Science you might have. Especially let us know if you’d like to add any data science books to our list. We are constantly updating it and making it better.

Data Science Rush is about creating an open community of people willing to learn Data Science and Machine Learning.

We also run a living course called Data Science Job that gives an overview of how to become a data scientist and find your first entry level data science job.

We constantly update the content of the course to make it up to date and relevant. Here’s an intro from the instructor, Przemek Chojecki.

You can sign up for Data Science Job course here.

Happy Learning!