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22 Top Data Science Books – Learn Data Science Like an Expert

Data Science has emerged to become one of the most paid and highly reputed domains for professionals. As we see more and more companies adopting data science applications in their businesses, there is a surge in the requirement for skilled data science professionals. If you are considering making a move in this domain, or are a data science expert who wants to remain on top of things, here is a list of books for you to keep the ball rolling. 

Top Data Science Books for Beginners

  1. Practical Statistics for Data Scientists
  2. Introduction to Probability
  3. Introduction to Machine Learning with Python: A Guide for Data Scientists
  4. Python for Data Analysis
  5. Python Data Science Handbook
  6. R for Data Science
  7. Understanding Machine Learning: From Theory to Algorithms
  8. Deep Learning
  9. Mining of Massive Datasets

Practical Statistics for Data Scientists – By Peter Bruce and Andrew Bruce

Data Science Books - Practical Statistics for data scientists

This book is ideal for absolute beginners. It covers a vast range of topics critical to the field of data science in an easy to understand language. You can learn a lot about statistics in data science and could cover in-depth on topics like randomisation, distribution, sampling etc. If you are starting from scratch, this book is for you. 

Introduction to Probability – By Joseph K. Blitzstein and Jessica Hwang

data science books - introduction to probability

Next in line after statistics is probability. It holds immense importance in the field of data science and this book will introduce you to the concepts by taking examples from real-life problems. If you have studied basic probability in school, this book is a build upon it. If you are studying probability for the very first time, you just need to spend some extra time with it. This book covers core concepts and will help you build a strong foundation for data science. 

Introduction to Machine Learning with Python: A Guide for Data Scientists – By Andreas C. Müller and Sarah Guido

data science books - introduction to ML with Python

Knowledge of Machine Learning is critical for a data science professional. This book helps you cover the basics of Machine Learning. If you practice along with the book for a substantial time, you would end up building machine learning models on your own. This book has all the examples with Python, but even if you do not have prior knowledge of Python programming language, you will be able to learn it through this book. This book is for beginners to understand the basics of ML and Python. It is recommended that when you are through with this book, you pick up an advanced level book to learn more about both Machine Learning and Python.

Python for Data Analysis – By Wes McKinney

data science books - python for data analysis

Apart from Machine Learning, Python is also a popular programming language in Data Analytics. Also, data analytics is critical to data science. Hence this book is a complete guide for beginners in data science to learn the concepts of Data Analytics with Python. The book is fast-paced yet simple. You can expect to be building real applications within a week with the help of this book. It is amazingly structured and organised for the readers and gives a peek into the world of data analysts and data scientists, and the kind of work the indulge into in their role. 

Python Data Science Handbook – By Jake VanderPlas

data science books - Python Data Science Handbook - By Jake VanderPlas

This book is a great recommendation for those who have covered the basics of Python and are ready to explore and work with Python libraries. Python Data Science Handbook is an in-depth guide into all standard Python libraries such as Pandas, Numpy, Matplotlib, Scikit-learn and more. 

R for Data Science – By Hadley Wickham and Garret Grolemund

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

R is another popular programming language for Data Science applications. For those who have worked on Python, the next step is to implement data science applications on R as well. R for Data Science is the perfect book to pick up coding in R. It covers the concepts of data exploration, wrangling, programming, modelling, and communication. 

Understanding Machine Learning: From Theory to Algorithms – By Shai Shalev-Shwartz and Shai Ben-David

data science books - Understanding Machine Learning: From Theory to Algorithms

This is a great book for those who want a deeper understanding into machine learning concepts and algorithms. It covers the foundation of Machine Learning, algorithms in ML, additional learning models and advanced theory. This book provides a great reference for implementing machine learning algorithms yourself. An extensive theory behind algorithms helps enhance the understanding and application of the same. 

Data Science Books for Advanced Level

Deep Learning – By Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This book is an amazing reference for deep learning algorithms. The book is not code-heavy but explains in-depth how to approach deep learning problems. The layout of the book is easy on the eyes with extensive use of bullets and images. Some of the topics covered in this book are introduction and explanation of the importance of deep learning; algorithms of backpropagation, convnets, recurrent neural nets; unsupervised deep learning; attention mechanisms and more.

Data Science Book for Data Mining

Mining of Massive Datasets – By Jure Leskovec, Anand Rajaraman, Jeff Ullman

This is an extremely comprehensive book developed on the basis of various Stanford courses on large scale data mining and network analysis. As the name suggests, it focusses on mining of very large datasets. One can learn to develop production-level models at a large scale with the help of this book. The major topics covered in this book are mining data streams, MapReduce, building recommendation systems, link analysis, dimensionality reduction, and more.

Other Important Data Science Books

  • The Elements of Statistical Learning — Data Mining, Inference, and Predictionby Trevor Hastie, Robert Tibshirani, Jerome Friedman

A valuable resource for anyone who is interested in statistics, this book uses a statistical approach to describe important ideas in different fields. It covers topics from supervised to unsupervised learning, neural networks, support vector machines, and more.

  • The Art of Statistics — How to Learn from Data, by David Spiegelhalter

This book, written by a world-renowned statistician, shows readers the art of deriving knowledge from raw data by focussing on the concepts and connections that shape math. This book not only shows how mathematicians solve statistical science to solve problems but also teaches us to think like them!

  • Data Science for Beginners, by Andrew Park

Created with a beginner in this field in mind, this powerful read delves deep into the fundamentals behind Python and Data Science. This data science book will help you discover everything you need to get started.

  • Data Science for Business — What You Need to Know about Data Mining and Data Analytic-Thinking, by Foster Provost and Tom Fawcett

Written by renowned data science experts, this book introduces the fundamentals of data science and also helps you walk through the data based analytical thinking. This approach is important for getting useful knowledge and business value from the data.

  • Build a Career in Data Science, by Emily Robinson and Jacqueline Nolis 

This data science book will be your companion in landing your first data science job and developing to a managerial role. It covers topics such as adapting to company needs, preparing for a management role, lifecycle of a typical data science project.

  • Clean Code — A Handbook of Agile Software Craftsmanship, by Robert C. Martin

This is a revolutionary data science book that has helped thousands of programmers in developing clean code. This book will allow you to think about what’s right about the code, what’s wrong with it, and will even give you a path to reassess your professional values.

  • The Art of Data Science — A Guide for Anyone Who Works With Data, by Roger D. Peng and Elizabeth Matsui

This data science book describes the process of analyzing data. Applicable to both practitioners and managers in data science, it provides an amazing overview of the data analysis workflow. It also gives an effective overview of how data analysis is primarily an art that involves iterative processes, with information learned at every step.

  • A Common-Sense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills (2nd Edition), by Jay Wengrow

This is a practical guide to understanding data structures and algorithms. It goes beyond theory and will help you in improving your programming skills. From learning how to use hash tables, trees, and graphs, to improving the efficiency of your code: you’ll learn it all in this data science book.

  • Deep Learning with Python, by Francois Chollet

Written by the creator of Keras and Google AI researcher, this book will introduce you to the field of deep learning using Python and Keras library. It consists of intuitive explanations and practical examples that will give you a good platform to understand the concept of deep learning.

  • Foundations of Deep Reinforcement Learning — Theory and Practice in Python, by Laura Graesser and Wah Loon Keng

This data science book is for anyone who has advanced knowledge of machine learning and wants to solve more complex problems using deep reinforcement learning. It is ideal for students and software engineers who have a working understanding of Python. 

  • Big Data — A Revolution That Will Transform How We Live, Work, and Think, by Victor Mayer-Schonberger

This book has been a finalist in the Financial Times Business Book of the Year. Big Data is an important and one of the first major books about this concept. It has 2 leading experts explaining what big data is, and how it will impact our lives in the years to come.

  • Fundamentals of Data Visualization — A Primer on Making Informative and Compelling Figures, by Claus O. Wilke

This data science book takes you through commonly encountered visualization problems and offers guidelines to turn large datasets into clear figures. It can help you understand the rationale behind effective visualization and also teach you to design more meaningful plots that get the right message across.

  • Storytelling with Data — A Data Visualization Guide for Business Professionals, by Cole Nussbaumer Knaflic

This data science book will teach you how to communicate effectively with data. It will help you understand the fundamentals of data visualization and is definitely a must-read book for anyone who wants to present information in a clear, brief, and graphical way.

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While self-study is an important aspect of learning new things and technologies, a structured approach with a certification course takes you a long way in your domain.

We’re sure these books will allow you to venture into the world of data science as you enter the year 2021. Further, we recommend that you take your enthusiasm for data science to the next level by getting a certificate from The University of Texas at Austin with their PG program in Data Science and Business Analytics by Texas Combs. It is a comprehensive 6-month online program that offers a 360-degree view of the core concepts in data science and business analytics. The video lectures are created by the university faculty and live mentoring sessions by industry experts take place every weekend in small groups. These sessions allow learners to get a better understanding of the internal working of the industry and also promote peer-to-peer interaction. 

To get more info, download the program brochure and begin your journey in the field of data science.

Further Reading

  1. Artificial Intelligence Books For Beginners | Top 17 Books of AI for Freshers
  2. Top 10 Machine Learning Books you can add to your 2022 wish list
  3. Machine Learning Tutorial For Complete Beginners | Learn Machine Learning with Python
  4. Data Science Tutorial For Beginners | Learn Data Science Complete Tutorial

1 Source: GreatLearning Blog

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