10 Best Data Science Books

By | January 1, 2020

When I started my data science journey, I weighed down different options to learn about the concepts –books, online courses, tutorials, etc. I did some basic courses which were handy because I could learn through apps sitting anywhere – on a bus, or a pizza store, or just relaxing with the family in front of the TV. But something was missing – I couldn’t figure out if I were missing some concepts or getting the entire big picture. Moreover, for a more serious, detailed, and long-term learning experience, it is always better to go with books. A book is also an excellent way to get away from gadgets for some time.

10 Best Data Science Books

So, I did thorough research, scanned through many editions, and found a few interesting and informative books that cover all the essential data science concepts at length. In this article, I want to share some key features of the top 10 data science books for the benefit of all. Feel free to share the name of a book you liked and are not on my list; I will be happy to review and include it.

1. Data Science from Scratch

Data Science from Scratch- First Principles with PythonThis was my first data science book and close to my heart – and that’s why it is at the top of my list. If you have read a few blogs about data science and have fundamental knowledge, this book is a great place to start. You will be taken to a different universe of data scientists, where a single problem will lead you to think in many ways about data. The book further teaches you Python (basics), statistics, linear algebra, probability, ML, and practically everything that you need for data science. It follows a hands-on approach with code examples wherever required. The book follows a do-it-yourself approach to enhance your thinking capabilities while also guiding you on how the author did what you can do differently.

Features:

Some key features of the book –

  • Ideal for beginners
  • Quick Python course, which the most popular language for Data Science
  • Self-explanatory and easy to follow, no other resources except those mentioned in the book are needed
  • An in-depth exploration of ML concepts and other areas of Data Science

You can buy this book here.

2. Data Science for Business

Data Science For BusinessThinking This is an ideal book for absolute beginners as well as those who have some idea about data science. If you are not convinced that Data Science is the right choice for you, this book changes your mind by giving practical examples of how data is mined and analyzed to achieve different results. The book first provides a high-level overview of the applications of data science and then moves on to technical details, although less. The book is more about thinking, creating, and analyzing business problems than going into the code details. The fundamental concepts are explained beautifully.

Features:

Some key features of the book –

  • Ideal for beginners as well as those who have some experience with Data Science
  • Great for technical as well as business persons
  • Gives an overall picture of data science and then goes into details
  • Topics are explained in an easy to understand manner with examples
  • The pace is perfect, and the content never feels overwhelming

You can buy this book here.

3. Data Smart – Using Data Science to transform information into insight

Data Smart: Using Data Science to Transform Information into InsightThis book starts with a bit of introduction. It then quickly moves on to details of ML algorithms, forecasting, analysis, and uses R in the later chapters to do some Data Science related programming. Until then, you have to grasp the concepts without any burden of coding. The book starts with an example of data that is in Microsoft Excel, hence knowing basic excel is a mandatory prerequisite for you to pick this book. In the first chapter itself, you will get a good overall picture of data science – from data transformation to visualization using different features of excel. You can read this book and then follow it with some courses on statistics, R or ML to further strengthen the concepts taught in the book.

Features:

Some key features of the book are –

  • Witty language and gentle introduction of concepts
  • Step by step learning by carefully creating the overall picture and then going into details
  • No need to know any new programming language or tools – most of us have worked on Excel at some point, or the other
  • Practical approach rather than too much of mathematical theory or notations makes it an easy read even for those without a programming background.

You can buy this book here.

4. Python Data Analytics

python data analytics: how to learn data science and use machine learning introduction to deep learning to master python for beginnersThis book is a perfect way to start your data science journey. If you have never read about or worked with Data Science, this is a must-have book for you. It completely focuses on Python and how the language can be used extensively for data science. It spoon-feeds every step of programming with Python and deals with a lot of packages like Numpy, Scipy, etc. that are used exclusively for analyzing and visualizing data. This book can teach you data science as well as Python. The author writes all the concepts simply and understandably. The examples help understand various concepts thoroughly.

Features:

Key features of the book –

  • Completely cover the most preferred programming language Python for Data Science stages
  • Great for beginners who want to build a strong foundation of Data Science and Python both.
  • Loads of examples, tutorials, practical exercises and explanation for libraries
  • Comprehensive and contains variety to keep the reader engaged
  • The book also introduces TensorFlow while teaching about ML algorithms

You can buy this book here.

6. R in Action

R in Action: Data Analysis and Graphics with RWhile most books you would find on data science are based on Python, R is an equally powerful language for the same. This book starts with a basic course about R and the different packages that are useful for data science and then gradually moves on to other concepts in a logical order so that beginners can fully understand the entire process. The parts of data management and statistics are quite detailed and well-organized for both beginner and intermediate level data scientists and business analysts. The range of topics covered is quite wide, and yet there is no pre-requisite for reading this book.

Features:

Key features of the book –

  • Shows the power of the R language with practical and relatable examples
  • Each chapter covers one algorithm/topic at length
  • The book touches upon advanced data science concepts as well
  • Covers both basic and advanced statistical methods as well as graphics along with real-world examples
  • Suitable for both beginners and experienced users

You can buy this book here.

6. Data Science for Dummies

Data Science For Dummies, 2nd Edition (For Dummies (Computers))This book doesn’t go much into the details but touches upon all the topics in an overall manner. It is a good book to know the vastness of data science and the concepts involved with big data. It acts as a quick reference when you are stuck or need to look up something. The book’s tone is friendly, witty, and funny and keeps you hooked. The book introduces loads of data science tools that are much useful to perform data analysis and visualization. It also dedicates a whole chapter for the applications of data science wherein readers can understand the importance of learning data science and how it can be applied to solve their business problems.

Features:

Some key features –

  • A handy reference that has all the concepts of data science
  • Encompasses the entire scope of data science thus making it suitable for beginners and intermediate level
  • Covers basics of few important data science tools like D3, R, Python, SQL, Excel, KNime, Excel, MapReduce, Tableau, SVG, Weka, etc.
  • Includes a handy data science cheat sheet as well as some datasets that you can use for practice
  • Part 5 of the book focuses on a few domains (journalism, environment, e-commerce etc…) and covers the related-use case in-depth for a complete end-to-end understanding

You can buy this book here.

7. Data Science for the layman 

Numsense! Data Science for the Layman: No Math AddedThis book is a great read for developers as well as business analysts. It is crisp and explains the concepts of statistics and data mining subtly from the first chapter itself. The author doesn’t waste much time on theory and starts with a practical, easy-to-understand example to cover the topics step by step. In most reviews, you would have read that this is an entry-level book; however, the book also touches upon some important advanced data science concepts like neural networks, supervised and unsupervised learning, and so on.

Features:

Some key features of the book are –

  • Doesn’t involve any coding, implementation or programming language usage
  • More of informational content for business leaders and managers to understand data science concepts
  • The well-organized flow of concepts that helps you to mentally picture how the entire process works without having to code or see the actual results
  • The book helps you develop an analytical mindset and enables you to think how a data scientist would and create questions that are helpful from a business perspective
  • Simple and friendly writing-style

You can buy this book here.

8.Data Science and Big Data Analytics

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting DataThe book is detailed and covers each aspect of data science with examples and case studies. The way the examples are presented is excellent. The book is for everybody – it starts with basics and then goes up to explain advanced concepts simply. There are a lot of colorful illustrations and pictures that make the book further appealing and interesting to read.

Features:

Key features –

  • Uses R as the base programming language
  • Introduces a good balance of mathematical concepts and advanced ML algorithms
  • The code and datasets can be easily downloaded from the links provided (Wiley website)
  • Though a little too technical, the author has tried to explain basic concepts very well to keep it interesting for readers
  • If you are planning for a data science certification, this book will certainly get you there

You can buy this book here.

9. Designing Data-Intensive Applications

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable SystemsThis book is a purely technical one, authored to help software engineers and architects build applications using the best tools. It discusses various tools and the use cases where each tool is the aptest one. The author breaks down all the complex concepts into small bits that are easy to understand. You will feel as if the author knows the next question in your mind and answers it just when you are pondering about it.

Features:

Unique features of the book –

  • Coherent and explains even the complex topics in a simple manner
  • Emphasizes the importance of using the right data structures for different types of problems
  • Not just implementation, the book also focuses on why data is so important today and how it impacts overall business
  • It helps the readers to develop a creative and analytical mindset by thinking beyond the implementation and answering questions like why, when, whom, where, how.
  • The book also focuses on performance, security, and the need to develop systems with sturdy architecture by including the necessary chapters for these.

You can buy this book here.

10. Pattern recognition and Machine Learning

Pattern Recognition and Machine Learning (Information Science and Statistics)Most of the previously mentioned books are for beginners and intermediate learners, however, this book is different. It contains in-depth information about topics that most other books won’t have. It is exhaustive and will challenge you at all levels. The author explains graphical modeling and pattern recognition with loads of mathematical equations, although you wouldn’t find any practical examples. This book is for serious learners and focuses on ML and not on the overall picture of data science.

Features:

Highlights –

  • Prior basic knowledge of statistics and algebra is a must.
  • Crystal clear and thorough explanation of advanced concepts
  • There are loads of equations, but you will find them rather interesting than overwhelming.
  • Some parts or sub-topics are left unexplained. Still, those are easy to find through other sources like the internet, articles, etc.
  • The books encourage self-learning and analysis of concepts leaving it to the reader to think analytically and arrive at solutions

You can buy this book here.

Summary

In my experience, rather than jumping into the fine technical details of each subtopic in the first go itself, it would be nice to understand why data science is a good choice and how the whole thing works from both technical and business perspectives. The overall picture will help you choose what’s most important and where your interest lies. In my list, Data science for business and Python data analytics are two books that can give you a good start. If you have read about data science from blogs or completed the basic courses, you might want to go with Data Science from scratch or data science for dummies followed by the other books in no particular order. Each book focuses on different parts of data science and will help you gain various perspectives. Happy reading!

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