8 Best Books for Data Science
I will cover some best books about data science in this article; it will be helpful for newbies to understand the world of data science as well as experienced data scientists to get more out of these books. I have mentioned the eight most popular books in this article, which are mostly recommended.
These books are very beneficial for you to gain knowledge and understanding of the important areas of data science like Machine learning, statistics, data science, Deep learning, and deployment.
Data Science and Big Data Analytics
This book is all about big data and covering the data analytics lifecycle. The concept of data science is very simple to understand as well as advanced analytics using Hadoop, MapReduce, and SQL are also explained in detail in this book.
An Introduction to Statistical Learning
The writers of this are Gareth James, Daniela Witten, Robert Tibshirani, and Trevor Hastie. Beginners can easily understand the concepts explained in the book. The concepts are explained with illustration and real-word scenarios which help to conceive the concept easily. Each lesson is a complete tutorial about implementing different modules in R.
Practical Statistics for Data Scientists
This book is written by Peter Bruce. Each concept in statistics is briefly explained which is helpful for data scientists. It is a book that closely connected the concept of statistics with that of data science. Although R is familiar with the book, it is still a good book for Python programmers to learn statistical concepts.
Good Charts, Tips Tools and Exercise for Better Data Visualization
This book is written by Scott Berinato. This book will help you in choosing the best chart for your data visualization. It will make your communication more effective while you have only a few minutes with the decision-maker.
Python Data Science Handbook
The author of this book is Jake VanderPlas. It is a comprehensive guide covering standard python libraries including NumPy, pandas, Scikit-Learn, Matplotlib.
This book is more helpful for those, who have already experience of python but need a little guide about the tools available for data science.
Machine Learning A Probabilistic Perspective
This book is written by Kevin Murphy. Mathematicians can easily understand this book because it is very useful for machine learning research.
Machine Learning Yearning
The author of this book is Andrew Ng. this is an advanced level book for data scientists, you can learn methods of structuring machine learning projects as well as set a goal for the data science team. After reading this book, you will be able to know the exact usage of Machine Learning. You can solve many complexities in implementing AI in the real world.
Data Science in Production
This book is written by Ben G Weber. This book covers three areas of data science including cloud deployment, machine learning models, and developing web endpoints. Data science is working to enhance the value of data in any organization. This book is good for professionals who want to work with data building technologies in various cloud environments and improve skills for applied data science.