Best Books on Python Data Science – Aside from the fact that Data Science is one of the highest-paid and most popular industries today, it’s also worth noting that it will remain creative and hard for another decade or more. As a result, plenty of data science employment will be available that will pay well and provide prospects for advancement.
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Data science is not only about computers; it also encompasses mathematics, probability, statistics, programming, machine learning, and much more. Learning data science via books can help you gain a comprehensive picture of data science.
Best Books on Python Data Science
1. Python Data Science Handbook by Jake VanderPlas published by O ‘Reilly
This book is ideal for people who are new to data analysis or data science and need a quick reference guide to all of the methods and library functions, as well as for those who want to improve their grasp on Python for data science and make it work for them
IPython (Interactive Python), Numpy, Data Manipulation with Pandas, Visualization with matplotlib, Supervised and some Unsupervised Machine Learning Algorithms using scikit-learn are covered in great length and depth in this book.
The quantity and quality of knowledge available on these subjects will greatly assist you in honing your abilities during the first stages of any data science project cycle.
2. Machine Learning with Python Cookbook by Chris Albon
Another Python book devoted to Data Science, Machine Learning, and Deep Learning. It begins with basic principles like linear regression and KNN before moving on to more advanced subjects like neural networks.
It also offers several wonderful practical examples that are properly explained and enable you to consolidate your learning, much like many other O’Reilly programming books.
3. Python Cookbook by David Beazley and Brian K. Jones
This is another Python book that may be used for various purposes. Python is a language that data scientists can learn. File/IO, data structures, networking, algorithms, and other fundamental subjects are covered in this book. These areas, including Data Science and Machine Learning, provide a solid foundation for any tech-related job.
This is a thorough book that teaches you how to use Python and universal programming ideas such as objects, classes, data structures, and algorithms that can be applied to any application.
4. Head First Statistics: A Brain-Friendly Guide by Dawn Griffiths
The book contains a wide range of statistics, beginning with descriptive statistics such as mean, median, mode, and standard deviation, and through probability and inferential statistics such as correlation and regression. You may have studied all of it in school if you were a science or commerce student, and the book is an excellent place to start to review what you’ve previously learned in depth.
There are several easy-to-remember drawings, graphics, and parts on the sides. In addition, you’ll discover some interesting real-life examples to keep you interested in the book. Overall, this is a fantastic book to start your data science adventure with.
5. Introduction to Probability by William Feller
If you had a math background in school, you might recall calculating the odds of receiving a spade or a heart from a deck of cards, and so on.
The explanations are clear and mirror real-world issues. This book is a must-have for everyone who has studied probability in school and wants to learn more about the fundamental ideas. If you’re learning probability for the first time, this book may help you lay a solid foundation in the fundamental principles, albeit you’ll have to devote more time to it.
6. Pattern recognition and machine learning by Christopher M. Bishop
There is something for everyone in this book, whether you are an undergraduate, graduate, or advanced level researcher. This book will cost you nothing if you have a Kindle membership. Get the international edition, which includes colorful pictures and graphs to make your reading experience even more enjoyable.
In terms of substance, this is one book that covers machine learning from top to bottom. It is comprehensive and teaches ideas straightforwardly using examples. Certain of the words may be difficult to grasp for some readers, but you should be able to get by with the help of other free resources such as online articles or movies. The book is a must-have for anybody interested in machine learning, particularly the mathematics (data analytics) section, which is extensive.
7. Naked Statistics by Charles Wheelan
This book highlights the beauty of numbers and brings them to life. The tone is casual and funny. You will not be bored while reading this book, nor will you feel the weight of arithmetic! With real-life examples, the author illustrates all fundamental and advanced statistical principles.
The book begins with fundamental concepts such as the normal distribution and the central theorem and goes on to more complicated real-world challenges such as correlating data analysis and machine learning.
8. Data Science and big data analytics
This book discusses big data and its importance in today’s technologically competitive world gently. The complete data analytics lifecycle is thoroughly discussed, along with a case study and engaging images, to understand how the system works in practice. The book’s structure and flow are excellent and well-organized. Because each stage is like a chapter in a book, you can quickly grasp the broader picture of how analytics works. Clustering, regression, association rules, and much more are covered in the book, along with easy, daily examples that anybody can connect to.
The reader is also exposed to advanced analytics utilizing MapReduce, Hadoop, and SQL.While the book does a fantastic job of explaining the fundamentals, several of these courses will benefit from some previous understanding of statistics so that you can dive right into the book.
9. R for Data Science by Hadley Wickham and Garrett Grolemund
R with data science is another book for beginners who want to learn data science using R. It explains not only the concepts of statistics but also the types of data that you might encounter in real life, how to transform it using concepts like median, average, standard deviation, and how to plot, filter, and clean it. In addition, the book will show you how actual data is chaotic and raw and how it is handled.
Data transformation is one of the most time-consuming activities. This book will provide you with information on various approaches for changing data for processing to extract relevant insights.
10. Big Data – A revolution by Viktor Mayer-Schnberger
This is not a technical book, but it will provide you with a comprehensive understanding of how big data is acquired, turned, and processed into sales and profits, even when people like us are unaware of it. In addition, it discusses how businesses use our data and the information we share on the internet to develop new business breakthroughs and solutions that make our lives easier and bring us all closer together.
It also discusses the risks and ramifications of doing so and how security measures are implemented to prevent data breaches or misuse.
11. The Data Science Handbook by Carl Shan, William Chen, Henry Wang, and Max Song
This is a book for advanced readers. However, you will be able to appreciate the material of the book if you have a basic understanding of statistics and data science from previous books or tutorials. It’s not only a technical book; it’s also a rapid reference, with information in the form of questions and answers from many top data scientists.
The questions are arranged to help you grasp each component of data science, such as data preparation, the relevance of large data, the automation process, and how data science is the digital world’s future. Although there are no genuine case studies in the book, if you approach it with a business attitude, you will learn a lot of tactics and suggestions from prominent data scientists who have been there and done that.