In this blog following topics will be covered

What are the Best Books for Data Science?

1. Overview of Data Science
   
2. Books to begin with Data Science
   
3. Books to help you Master Data Science

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Overview of Data Science

• Data science is the process of combining experts, programming skills, knowledge of mathematics, and statistics from the field to gain meaningful insights from data. 
  
• Data science practitioners use machine learning algorithms for numbers, text, images, video, audio, and more to develop artificial intelligence (AI) systems to perform tasks required by the humanities. 
  
• These systems generate statistics that can be translated into clear business value for the consequences and for business users.

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• Aside from the fact that data science is one of the highest paid and most popular fields today, it should be noted that it will be very innovative and challenging for a decade or more. 
• There will be enough data science jobs to get beautiful salaries and opportunities to grow.
• Since data science includes not only computing, but also mathematics, probability, statistics, programming, machine learning and much more, studying data science through books can help you gain a holistic view of data science.
• Understand ML Concept as well. The book contains examples of Python, but you do not need any prior knowledge of mathematics or programming languages ​​to read this book.
• This book is for beginners and covers the basics in detail. However, just reading this book is not enough, because you will check the ML and coding.

1. Head First Statistics: A Brain-Friendly Guide

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   • Like other books by Headfast, this book's tone and friendliness are excellent for dialogue and data science. 
     
   • The book covers a wide range of statistics, including average, average, mode, and standard deviation - followed by Probability, Correlation, and Regression. 
     
   • Easy to remember The pages have lots of images, graphics and bits. You can find some good real life examples to include yourself in the book. 
     
   • Overall a great book to start your data science journey.

2. Practical Statistics for Data Scientists

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   • If you are a beginner, this book will give you a good overview of all the concepts you need to learn to master data science. 
     
   • The book is not very detailed, but gives good information about all the major concepts like randomization, modeling, distribution and model bias. 
     
   • The book surprises one with a survey of ML models.
     
   • This book covers all the topics required for Data Science. 
     
   • However, this is a quick and easy reference because the lack of explanations and examples is not enough to study the concepts in depth.

3. Introduction to Probability

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   • If you have a math background at school, you may remember calculating your chances of getting a spade or heart from a pack of cards.
     
   • This is the best book to know about probability. The descriptions are very clean and similar to real life problems. 
     
   • If you have studied probability in school, this book will further enhance your knowledge of the basics. 
     
   • If you are going to study probability for the first time - this book may help you build a strong foundation on important ideas, however you will need to work with the book for a while.
     
   • This book has been one of the most popular books for almost 5 decades and this is definitely another reason to have it on your bookshelf.

4. Introduction to Machine Learning with Python: A Guide for Data Scientists

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   • This is the book that will get you started on your ML journey with Python. 
     
   • Ideas are illustrated with enough examples for better understanding as a normal human being. The voice is friendly, heartfelt and easy to understand. 
     
   • ML is a very complex subject, however, after training with the book, you can create your own ML models. 
   • You can better understand ML's comments. The book contains examples of Python, but you do not need any prior knowledge of mathematics or programming languages ​​to read this book.
   • This book is for beginners and covers the basics in detail. However, reading this book alone is not enough, as you will examine ML and coding in more depth.

5. Python Machine Learning By Example

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   • As the name suggests, this book is an easy way to get into machine learning. 
     
   • The book begins with Python and Machine Learning in a comprehensive and interesting way, with some great examples such as spy email detection using Python, regression and predictions using tree-based methods

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Books to help you Master Data Science

1. Pattern recognition and machine learning

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   • This book is for everyone of all ages, whether you are a bachelor, graduate or advanced level researcher. 
     If you have a Kindle subscription, this book will cost you nothing. 
     
   • Get the international edition with colorful pictures and illustrations that will make your reading experience absolutely worthwhile.
     
   • Coming to the content, this is a book that covers the outside of machine learning. This is done by simply explaining the ideas using examples. 
     
   • Some words are difficult for some readers to understand, but you can find other resources, such as web articles or videos, using generic sources. 
     
   • The book is a must if you are serious about engaging in machine learning, especially since the mathematics (data analysis) part is done in nature.
     
   • Although you can use the book for self-study, it is best to read it along with some machine learning courses.

2. Python for data analysis

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   • As the name implies, the book covers all methods of data analysis. 
     
   • This is a good start for a beginner before going into the role of Python in data startup and statistics, and includes the basics of Python. 
     
   • The book is fast and everything is explained in a very simple way. 
     
   • You can create some original apps within a week of reading the book. 

3. Data Science and Big Data Analysis

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   • This book slowly introduces big data, how important it is in today’s digital competitive world. 
     
   • You can see the practical operation of the whole system as it describes in detail the complete data analysis along with life cycle case study and attractive conditions. The structure and flow of the book is excellent and well organized. 
     
   • Each step is like a chapter in a book so you can easily understand the big picture of how the analysis goes. 
     In the book one can illustrate simple and everyday examples using clustering, regression, association rules and more. 
     
   • It also introduces the reader to advanced analytics using MapReduce, Hadoop and SQL.

4. R for Data Science

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   • Another book for beginners who wants to learn data science with R, which explains not only the concepts of statistics, but also the data you see in real life, how to change this with concepts like average, mean, standard Deviation. 
     
   • The book will help you understand how confusing real data can be and how real it is and how it is processed. 
     
   • Data conversion is one of the most time consuming tasks, and this book will help you gain a lot of knowledge about the different methods of converting data for processing so that you can gain meaningful insights. 
     
   • If you want to learn R before you start the book, you can do it with simple online courses, however, you can start now as the book has the necessary basics.

5. Storytelling with data

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   • Telling anything as a story and showing it as graphics will easily fit in our minds and stay there forever. 
     
   • This book deals with the basic concepts of data visualization to better understand how to make the best use of the vast majority of data available in the real world. 
     
   • The author's method of explaining each idea is quite unique, because he tells it in the form of a remarkable story. 
     
   • You never realize how many ideas you can get in a day reading a book - getting to know the environment and the audience better, using the right map for the right situation, identifying and removing clutter to get only important information and giving away the most important parts of the data - and much more.

You can give a guide to this book or for topics you may be missing while searching for online courses.
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