In an era of bootcamps and learn-xyz-in-7-days, books still remain to be the most valuable resource one can avail. Data Science is still gaining a lot of popularity and getting a lot of attention. Hence it is the perfect time for you to get into this field.

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Data Science was famously declared to be the sexiest job of the 21st century in a major publication from Harvard Business Review by Thomas H. Davenport and DJ Patil where they talked about how data is becoming more and more accessible and easily tameable with the emergence of technology which is focused on big data. In fact, this is the trend in terms of web searches for the same in the last 5 years –

As we can see, the popularity has been somewhat going up over the last 5 years, and rightfully so maybe. With all the technology now being focused on greater seamless connectivity and real-time analytics, it makes sense to see this jump.

If you want to get a proper introduction to Data Science kindly check out this particular article –

There are thousands and thousands of online courses, bootcamps ad tutorials now, but which ones are really valuable?
In this article, we will go through a list of top 10 books on Data Science which will definitely come in handy irrespective of whether you are just beginning your data science journey or if you are a professional. Now let’s begin!

1. Python for Data Analysis

By : Wes McKinney
Tags: Beginners, Data Analysis, Python

Written by Wes McKinney, creator of the pandas framework for python, this book is a practical introduction to scientific computing in python and contains how-to examples for learning the full data preprocessing pipeline – data gathering, cleaning, manipulating and processing. You will get familiarized with a lot of different widely used python libraries with practical real-world case studies.

Some topics included in the book are-

• Using the IPython interactive shell
• Numpy – from basics to advanced
• Pandas – from basics to advanced
• Data Visualization

Must check out : The book is filled with case studies and data wrangling exercises which not only is good for practise but also shows you the proper way to deal with a data problem, hence teaching you how to intuitively approach such a problem.

Amazon Link : Python for Data Analysis
Amazon India Link: Python for Data Analysis

2. Practical Statistics for Data Scientists

By : Peter C. Bruce, Andrew Bruce, and Peter Gedeck
Tags: Intermediate, Statistics, Python, R

We all know statistics is a key part of data science. But it is also a subject which is ignored by most people simply because it may seem a little difficult to understand. This book is a sort of a practical guide and does a brilliant job of structuring and packing everything in statistics into bite-sized pieces for you to quickly consume, from a data science perspective.
P.S. – do not go by the name, it is not only meant for the pros but everyone!

Some topics included in the book are-

• Exploratory Data Analysis and its significance
• Experimental Design Principles
• Classification and Regression Techniques
• Statistical Tests

Must check out : The book contains at least 50 essential concepts which have been explained and implemented using R and Python programming languages!

Amazon Link : Practical Statistics for Data Scientists
Amazon India Link: Practical Statistics for Data Scientists

3. Introduction to Machine Learning with Python

By :  Andreas C. Müller and Sarah Guido
Tags: Beginner, Machine Learning, Python

This book gives a very practical introduction to Machine Learning with the help of examples in Python Programming Language. It will guide you in a step-by-step manner to build machine learning models, with special focus on libraries like numpy and scikit-learn. The authors focus more on the practicality so that you easily pick up the skills in data wrangling and model building.

Some topics included in the book are-

• Fundamentals of Machine Learning
• Overview of various Machine Learning Algorithms
• Data Visualization
• Natural Language Processing
• Machine Learning Pipelining

Amazon Link : Introduction to Machine Learning with Python
Amazon India Link: Introduction to Machine Learning with Python

4. An Introduction to Statistical Learning

By : Daniela Witten, Trevor Hastie, Gareth M. James and Robert Tibshirani
Tags: Intermediate, Statistics, R

This book is a “how-to” guide to the most prominent statistical learning methods and contains intuitive explanations on how to implement cutting edge statistical and machine learning algorithms. You will find plenty of theoretical and mathematical reasoning which will help in building a strong core for machine learning specifically. The best thing about this book is that you can pick it whenever and just go with it. You don’t need to be an expert in the field to grasp the concepts.

Some topics included in the book are-

• Classification and Regression techniques
• Regularization
• Model Selection
• Unsupervised Methods
• PCA

Must check out : The book contains several labs making use of R programming language with detailed explanations and implementations of real-world use cases!

Amazon Link : An Introduction to Statistical Learning
Amazon India Link: An Introduction to Statistical Learning

5. Data Science from Scratch

By : Joel Grus
Tags: Intermediate, Data Science, Python

People tend to think that learning how to use libraries, modules and various other tools is enough to become a Data Scientist. The aim of this book is to help you actually understand Data Science. You will get introduced to many of the fundamentals in Data Science and you will see how the algorithms actually work as you will learn how to code them up from scratch. This book is a great catch as it works on messy data and shows how to extract insights from that.

Some topics included in the book are-

• Data Science Fundamentals
• Data Preprocessing pipeline
• Machine Learning Algorithms
• Database Systems

Must check out : The book deep-dives into the explanations of the machine learning algorithms which are super cool and interesting. And not only that but you also get to implement them from scratch so as to really understand them!

Amazon Link: Data Science from Scratch
Amazon India Link: Data Science from Scratch

6. Deep Learning

By : Ian Goodfellow, Yoshua Bengio and Aaron Courville
Tags: Intermediate, Deep Learning

Deep Learning is a form of Machine Learning which primarily deals with a particular form of algorithmic learning, something called Neural Networks. It was mainly a topic of research until the last few years, until advancements in computer science made development of powerful computing machines possible. This book is perhaps the best book if you want to work in the field of Deep Learning. It provides an introduction to almost all the topics in Deep Learning covering not only the conceptual aspects but also the mathematical aspects of each and every thing.

Some topics included in the book are-

• Regularization
• Neural Networks
• Optimization Algorithms
• Sequence Modeling

Must check out: There is a FREE version online as well! The chapters are uploaded in compliance with MIT Press policies, along with supplementary resources for both readers and lecturers. You can find the chapters here – deeplearningbook .

Amazon Link : Deep Learning
Amazon India Link: Deep Learning

7. Hands on Machine Learning

By : Aurelien Geron
Tags: Intermediate, Machine Learning, Python

This book provides you with intuitive explanations of various machine learning algorithms along with their implementations and practical examples using Scikit-Learn, Keras and TensorFlow – some of the most widely used frameworks in the world of machine learning. It goes from the very simple algorithms like Linear Regression progressing to the advanced Deep Learning algorithms. After reading this book, you will know how to tackle most machine learning problems!

Some topics included in the book are-

• Supervised Machine Learning algorithms
• Unsupervised Machine Learning algorithms
• Data Visualization
• TensorFlow implementations of various algorithms

Must check out : The book focuses heavily on implementations using TensorFlow which are very useful and insightful for when you are building your own models. It shows how various parameters and hyperparameters affect your machine learning or deep learning model. The book has some especially useful exercises so don’t skip them!

Amazon Link : Hands on Machine Learning
Amazon India Link: Hands on Machine Learning

8. Deep Learning with Python

By : François Chollet
Tags: Advanced, Deep Learning, Python

Deep Learning with Python is another book on Deep Learning, but it focuses heavily on a hands-on approach to Deep Learning. It works best if you have both the books, the Deep Learning book mentioned above at #5 for theory, and this one for a more programmatic approach. The author François Chollet is none other than the creator of one of the best and most widely used deep learning frameworks called Keras. The algorithmic implementations and programming exercises quite naturally involve the usage of Keras to a great extent.

Some topics included in the book are-

• Neural Network Implementations
• Computer Vision applications
• Sequence Modeling
• Generative Adversarial Networks

Must check out : The book contains implementations of all the deep learning algorithms which are there and a few nice tips and tricks to make things work using keras, with all these examples being relatively easy to adapt to one’s own use-cases.

Amazon Link : Deep Learning with Python
Amazon India Link: Deep Learning with Python

9. The Elements of Statistical Learning

By : Trevor Hastie, Robert Tibshirani and Jerome H. Friedman
Tags: Advanced, Statistics

This book is an advanced version of the book mentioned above at #4. It might not be to everyone’s liking as the focus is of this book is to provide you with mathematical reasoning and statistical intuition behind complex topics. Machine Learning is taught from a statistical perspective and the book does a wonderful job of tying together how and why algorithms work.

Some topics included in the book are-

• Supervised Learning
• Regularization
• Model Assessment and Selection
• Neural Networks

Must check out : This book also has a FREE version along with some supplementary data sets here. The exercises at the end of each chapter are super helpful and provide a good sense of variety in terms of problems. The chapters on Boosting and SVMs are really detailed and well written.

Amazon Link : The Elements of Statistical Learning
Amazon India Link: The Elements of Statistical Learning

10. Data Science for Business

By : Tom Fawcett
Tags: Advanced, Business Analytics

If you are interested in improving your business-analytical thinking, which is very useful in extracting business insights and taking data-driven decisions, then this is the book for you. This book even has content on how to communicate with stakeholders in a business and how to work on a data science problem in a proper data science team.

Some of the topics included in the book are-

• Data-Analytic Thinking
• Model Selection
• Decision Analytic Thinking
• Data Science and Business Strategy

Must check out : The book is filled with practical real-world applications and examples which the reader will find super interesting as they show how a data-driven business strategy influences decision making and the business outcome. There is also a Proposal Review guide which will help you in assessing a data science project before taking it up!

Amazon Link : Data Science for Business
Amazon India Link: Data Science for Business

Additional Resources –

These are additional books which you can pick up. From learning Python and R to pattern recognition and even data visualization. All these books mentioned so far and below are top of the top and I suggest you start with them as soon as you can!

Here is a Data Science learning path which was designed by The Data Science Portal from online courses, books and other material which were found to be the most popular and helpful for the year 2020 –

Along with this, here are a few topics which have been explained here already by The Data Science Portal which you might want to check out!

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In this article, I wanted to give the reader a list of the best books in Data Science and Machine Learning so that they have a good amount of resources. I hope you liked the post, kindly like / subscribe / share for more of such content.

Thank you for reading!