Contents

## 1. Objective

In this tutorial, you will learn about the best books on data science. That will help you in becoming Data science expert. You will learn about various data science books being suggested by experts. Such roles like Data Analyst, Data Scientist, Python Developer, Software Trainer(Python). You will get the best books to learn Data Science, R, Python and Machine Learning.

## 2.Introduction to Data Science and Books on Data Science

Data science is not only analyzing data but it is much more than this. There exist people who enjoy analyzing the data. They could spend all day looking at histograms and averages. It offers a range of roles and requires a range of skills.

In today’s world, it is very much popular. Also, there is a huge amount of data is present which is generated each day in different fields. Thus, users need to perform some operations by analyzing the data set. Then find something useful from that data.

So here is our recommendation for the best Books on Data Science and become a master of the technology.

### 2.1 R for Data Science

#### a. R Cookbook by Paul Teetor

This book offers a technique. We use this technique to analyze data. Its main focus is only on practical aspects. It covers a wide range of topics such as statistics, probability, time series analysis etc. I will suggest you more books on data science.

#### b. Advanced R by Hadley Wickham

This book is basically for peoples who are interested in data science. it also describes how it works that creates a major difference in 3 analytical tools called R, SAS, SPSS.

It gives a step by step explanation, with code snippets that you can try yourself as you read. It’s neither for R beginners nor any readers new to programming.

It is for the readers who want to advance their skills. And one who already has command of sub-setting, vectorization, and R data structures.

#### c. The Art Of Programming by Norman Matloff

In this book, we will learn how to do software development. Along with this from basic data types and data structure to advanced topics. We don’t need any type of statistical knowledge. And your programming skills can range from amateur to genius.

#### d. Learning RStudio For R Statistical Computing by Mark P.J.van der Loo

The book is for developers and analysts. Basically for those who want to do R statistical development using RStudio functionality. You can and create and manage statistical analysis projects, generate reports and graphics. This book also teaches you how to use R on the popular IDE RStudio rather than on the standard R software. As we have more books on data science. We will go through each and every book.

#### e. Practical Data Science with R by Nina Zumel & John Mount

We have too many books on Data Science. But, this book is best for freshers. Those who are yet to enter in the Analytics industry. As the author focuses on establishing a connection between ML. Also on its impact on real-world activities.

The main focus of this book is on data science methods and their applications in real world.

It’s different in itself. None of the books talk about real-world challenges, but it does.

#### f. A Handbook of programming with R by Garrett Grolemund

Those people who are new to R, this book is best for them. Here you will learn how to write functions and loops in R. rather than juggling with packages. This book also teaches you to learn how to assemble and disassemble data objects. Also, how to write your own functions. The book language is simple to understand to examples can be reproduced.

### 2.2 Python for Data Science

#### a. Advanced Machine Learning with Python

Basically, this book is written by John Hearty. Also, it’s a definite read for every machine learning enthusiasts. Moreover, it lets you rise above the basics of ML techniques. And also dive into unsupervised methods. Generally, this is a book you would want to read to improve your ranks. The author lays equal emphasis on theoretical as well practical aspects.

#### b. Building Machine Learning Systems with Python

Generally, this book is written by Willi Richert, Luis Pedro Coelho. As we have seen to many books on on data science. Although, in this book, the authors have chosen a path of, starting with basics. Also in explaining the concepts through projects and ending on a high note. Hence, I will suggest you this book to newbie python machine learning enthusiasts. Also, it covers more topics. It’s easy to understand and fast to implement textbook.

#### c. Programming Collective Intelligence

Basically, this book is written by Toby Segaran. The most important thing about this books is it’s an interesting title. Also, this book is meant to introduce you to several ML algorithms. Such as SVM, trees, clustering, optimization etc using interesting examples and used cases. Also, this is a book which is best suited for people new to ML in python. Python, itself is known for its incredible ML libraries. And it support should make it easy for you to learn these concepts faster.

#### d. Python Machine Learning

Generally, this book is written by Sebastian Raschka. Also, it’s one of the most comprehensive book on data science. Also, I’ve found on ML in Python. Moreover, the author explains every crucial detail. That we need to know about machine learning. He takes a step-wise approach to explaining the concepts supported by various examples. Also, this book covers more topics.

#### e. Introduction to Machine Learning with Python

Basically, this book is written by Andreas Muller and Sarah Guido. It’s meant to help beginners to get started with machine learning. Also, it teaches us to build ML models in python sci-kit-learn from scratch. It assumes no prior knowledge. Hence it’s best suited for people with no prior python or ML knowledge. In addition, it also covers advanced methods for model evaluation and parameter tuning. Also methods for working with text-data, text-specific processing techniques etc.

#### f. Python for Data Analysis

If you want to get started with data analysis with Python? First, get your hands on this data analysis guide by W Mckinney, the main author of Pandas library. Although, there isn’t any online course as comprehensive as this book. Also, this book covers all aspects of data analysis. That is from manipulating, processing, cleaning, visualization and crunching data in Python. But, if you are a new to data science python, it’s a must-read for you. It’s power-packed with case studies from various domains.

#### g. Mastering Python for Data Science

Generally, this book is written by Samir Madhavan. Also, this book starts with an introduction to data structures in Numpy & Pandas. It provides a useful description of importing data. That is from various sources into these structures. Moreover, you will learn to perform linear algebra in Python. Thus it makes analysis by using inferential statistics.

### 2.3 Machine Learning and Statistics for Data Science

**Machine learning**

It is the ability of a machine to generalize knowledge from data—call it learning. Without data, there are little machines can learn.

To push data science to increase relevance, a catalyst is an important thing. It increases machine learning usage more in many different industries. This language is only as good as the data is given. It has an ability of algorithms to consume it. My expectation is that moving forward basic levels of machine learning. It will become a standard need for data scientists.

#### a. Programming Collective Intelligence

It is one of the best books to start learning machine learning. Also, if there is one book to choose on machine learning – it is this one. Moreover, I haven’t met a data scientist yet who has read this book and does not recommend to keep it on your bookshelf.

#### b. Machine Learning For Hackers

Generally, this book is written by Drew Conway and John Myles White. It is majorly based on data analysis. Also, this book is best suited for beginners. Also for those who are having basic knowledge of R. Moreover, it covers the use of advanced R in data wrangling. Also, it has interesting case studies. That will help you to understand the importance of using machine learning algorithms.

#### c. The Elements of Statistical Learning

Basically, this book is written by Trevor Hastie, Robert Tibshirani, Jerome Friedman. Also, this book explains the machine learning algorithms mathematically from a statistical perspective. It also provides a powerful world created by statistics and machine learning. But this books lays emphasis on mathematical derivations. That is to define the underlying logic behind an algorithm. Moreover, this book demands a rudimentary understanding of linear algebra.

#### d. Pattern Recognition and Machine Learning

As this book is written by Christopher M Bishop. Also, this book serves as an excellent reference for students. Although, this book assumes the knowledge of linear algebra and multivariate calculus. It also provides a comprehensive introduction to statistical pattern recognition techniques.

### 2.4 Artificial Intelligence for Data Science

#### a. Artificial Intelligence for Humans

Generally, this book is written by Jeff Heaton. It is a very useful book. As it helps in understanding the basics of an artificial intelligence algorithm. Also, it explains us in detail these algorithms using interesting examples and cases. And it’s needless to say, this book requires good commands over mathematics. Otherwise, you’ll have a tough time deciphering the equations.

#### b. The Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of Human Mind

As this book is written by Marvin Minsky. In this particular book, Marvin offers a fascinating model of how our mind works. Also, he tries his best to infer the future of human mind by examining different forms of mental activity.

Moreover, you’ll find path-breaking research findings. That is where Marvin has challenged the status quo. Also, this book is great to develop perspective and become aware of a present to a future transition of A.I.

#### c. Paradigm of Artificial Intelligence Programming

As we have so many books on data science. But, this book teaches advanced common techniques to build major A.I systems. It also provides the practical aspects. And teaches its readers the method to build and debug robust practical programs. Moreover, it also demonstrates superior programming style and essential AI concepts.

## 3. Conclusion

This blog covers best books on Data Science. It will definitely help you to learn Data Science efficiently. Keep Studying and Keep learning.