Data science is the revolutionary tech for gathering knowledge from data that are either structured or unstructured. By using scientific ways, algorithms, and many more ways, different data are collected to make new learning. It is considered as the 4th paradigm of science. Various data science books, publications, thesis papers, and magazines are available online, which declare the glory, present basement, future destination, and ways to be with Data Science.
Why is data science required? To make a very important and careful decision based on a lot of information or data in bigger fields like industries, marketing, etc. Data Science is the only solution. The data scientists, especially those who are a Ph.D. holder, are highly demanding in these fields, and he is highly paid. This is just to show the importance and value of data science.
Best Data Science Books
As per the above discussion, we can easily understand the requirement of learning Data Science. Thereby we have gathered some of the best data science books that are available online to make the study of the data science knowledge seekers an easier one. We hope these books will be a very good basement for the upcoming data scientists.
1. Introducing Data Science
The starting of data science study should be well organized; thus, this book is written to teach introductory data science in an organized fashion. No doubt, this book is different from other data science books available. The book highlights the main factors and benefits which can attract a new reader in the data science world. A discussion of machine learning and the process of data science is there in the book.
Table of Contents
 Data Science in a Big Data World
 Data Science Process
 Machine Learning
 Handling Large Data on a Single Computer
 First Steps in Big Data
 Join the NoSQL Movement
 The Rise of Graph Database
 Text Mining and Text Analytics
 Data Visualization to the EndUser
2. Getting Started With Data Science
If you want to start with Data Science without losing any interest, then this book is the perfect book among all other Data Science books. Numerous interesting and important logics are well discussed in the book. You can know to speak hypothetically and understand many important decisionmaking processes. The whole data science is made understandable with different graphical presentations and tables.
Table of Contents
 The Bazaar of Storytellers
 Data in the 27/7 connected World
 The Deliverable
 Serving Tables
 Graphic Details
 Hypothetically Speaking
 Why Tall Parents Don’t Have Even Taller Children
 To Be or Not To Be
 Categorically Speaking About Categorical Data
 Spatial Data Analytics
 Doing Serious Time with Time Series
 Data Mining for Gold
3. Data Science: Concepts and Practice
All the basic data science books which are to clear the concept of the topic are vast and detailed. This data science book is also the same, where different topics related to data science are also brought to make the understanding easy and fruitful one. Besides many important topics, you can learn how to detect anomalies and how to select features. You will also get the basic knowledge to start with Rapid Miner.
Table of Contents
 AI, Machine Learning and Data Science
 Data Science Process
 Data Exploration
 Classification
 Regression Methods
 Association Analysis
 Clustering
 Model Evaluation
 Text Mining
 Deep Learning
 Recommended Engines
 Time Series Forecasting
 Anomaly Detection
 Feature Selection
 Getting Started with Rapid Miner
4. Data Science from Scratch
Another great collection from O’Reilly Data Science Books that teaches the topic very interestingly. The gradual development of the book will surely impress you. Many important topics like Linear Algebra, Machine Learning, Neural Network, etc. are very clearly discussed in the book. You can learn Natural language processing and know how to analyze the network.
Table of Contents
 The Ascendance of Data
 A Crash Course in Python
 Visualization Data
 Linear Algebra
 Statistics
 Probability
 Hypothesis and Interface
 Gradient Descent
 Getting Data
 Working with Data
 Machine Learning
 KNearest Neighbors
 Naive Bayes
 Simple Linear Regression
 Multiple Regression
 etc.
5. Beginners’ Guide to Analytics
Beginners’ Guide to Analysis is a precise and powerful book. If you are a true beginner in Analytics or Data Science, then this book is the right choice. The book starts by giving the application of analytics in different fields of industries like Retail, ECommerce, Finance, Sports, etc. The newbies will come to know about different aspects and future in the data science field after reading this book. You will be introduced with different free and paid tools that you need in Analytics. Finally, you get good teaching on Big Data.
Table of Contents
 What is Analytics
 How is Analytics Used?
 Career in Analytics
 Popular Analytics Tools
 Future of Analytics
 Introduction to Big Data
6. Data Science at the Command Line
Data Science at the Command Line is a collection of O’Reilly. Unlike other data science books, this book starts with defining the command line. Then gradually, it shows different aspects of data science. All the topics are well covered, and you will get a systematic description of all. Like, you will get an overview of all the topics before you go deeper. At the end of the book, you will get a list where different tools of commandline are given.
Table of Contents
 What is the Command Line
 Getting Started
 Obtaining Data
 Getting Reusable CommandLine Tools
 Scrubbing Data
 Managing Your Data Workflow
 Exploring Data
 Parallel Pipelines
 Modeling Data
 List of CommandLine Tools
7. The Field Guide to Data Science
This book is an excellent guide for readers who want to know data science properly and genuinely. The beginning of the book contains a concise and concrete description of the topic. Then there are many guidelines and ways to go deep in data science. You can learn basic machine learning and the relation to data science. The book will give you a clear idea about the farreaching and bright future of data science, which will motivate and increase your interest in the field.
Table of Contents
 The Short Version The Core Concepts of Data Science
 Start Here for the Basics
 Take off the Training Wheels
 Life in the Trenches
 Putting it all Together
 The Feature of Data Science
8. Data Science: Theories, Models, Algorithms, and Analytics
This book is a source of knowledge where you get an indepth dissection of Data Science. Starting from theoretical knowledge, you can learn data science algorithms, tools, and analytics in the book. All the topics are named differently and interestingly. You will get clear ideas about optimal digital portfolios and become an expert in analyzing clusters.
Table of Contents
 The Art of Data Science
 The Very Beginning: Got Math?
 Open Source Modeling in R
 More: Data Handling and Other Useful Things
 Being Mean with Variance: Markowitz Optimization
 Learning from Experience: Bayes Theorem
 More than Words: Extracting Information from News
 Virulent Products: thaw Bass Model
 Extracting Dimensions: Discriminant and Factor Analysis
 Bidding it Up: Auctions
 Truncate and Estimate: Limited Dependent Variables
 Riding the Wave: Fourier Analysis
 Making Connections: Networking Theory
 Statical Brains: Neural Networks
 Zero or One: Optimal Digital Portfolios
 Against the Odds: the Mathematics of Gambling
 In the Same Boat: Cluster Analysis and Prediction Trees
9. The White Book of Big Data
Out of all big data books, this book can be considered as the best one, and you can claim it as a bible of big data. This big data book gives the idea and guidelines for business analytics. It is a guide to run a bigger business where you can manage your business professionally using big data. Different adoption process and improving the system of the system with businesses are given in the book.
Table of Contents
 What is Big Data?
 What Does Big Data Mean for the business?
 Clearing Big Data Hurdles
 Adoption Approaches
 Changing Role of the Executing Team
 Rise of the Data Scientist
 The Future of Big Data
 Big Data Speak
10. Big Data, Data Mining, and Machine Learning
The book is a combo of three important technologies named Big Data, Data Mining, and Machine learning. In the first part of the book discusses Hardware, Distributed System, and Analytical Tools. Then the book emphasizes the way to turn data into business. Finally, different case studies are there in the final chapter, where learning from incidents from wellknown industries is included.
Table of Contents
 Part I: The Computing Environment

 Hardware
 Distributed System
 Analytical Tools

 Part II: Turning Data into Business Value

 Predictive Modeling
 Common Predictive Modeling Techniques
 Segmentation
 Incremental Response Modeling
 Time Series Data Mining
 Recommendation System
 Text Analytics

 Success Stories of Putting It All Together
 Case Study of Large U.S.Based Financial Service Company
 Case Study of Major Health Care Provider
 Case Study of Technology Manufacturer
 Case Study of Online Brand Management
 Case Study of HighTech Product Manufacturer
 Looking to the Future
11. Going Pro in Data Science
Who does not want to become a pro? O’Reilly collection has published this ‘Going Pro in Data Science’ for those guys. The book will show you the data science of present days and upcoming days. You can know how to become confident, which is essential to become a pro. After reading this book, you can learn how to think, build, dream, design data science, obviously like a pro. The book increases the skill through realistic means and fulfills the realistic expectations.
Table of Contents
 Finding Signals in Noise
 How to Get Competitive Advantage Using Data Science
 What to Look for in a Data Scientist
 How to Think Like a Data Scientist
 How to Write Code
 How to Be Agile
 How to Survive Your Organization
 The Road Ahead
12. Mastering Python for Data Science
Python is one of the ruling languages of computer science. This book teaches you to explore the data science world via python. The book is a perfect guide to perfect data sensing. You can consider the book as one of the best data science or big data books. Many tricks and tips for doing many hard works are given in the book. You can estimate many of your important calculations before going to a big job after you finish this book.
Table of Contents
 Getting Started with Raw Data
 Inferential Statistics
 Finding a Needle in Haystack
 Advanced Visualization Tools for decision making
 Uncovering Machine Learning
 Performing Predictions with a Linear Regression
 Estimating the Likelihood of Events
 Generating Recommendations with Collaborative Filtering
 Pushing Boundaries with Ensemble Models
 Applying Segmentation with kmeans Clustering
 Analyzing Unstructured Data with Text Mining
 Leveraging Python int the World of Big Data
13. Python Data Science Handbook
The O’Reilly collection always brings awesome and outstanding books. They also catered for a book that discussed Data Science through Python. However, the book is so precise and comprehensive that it is named as the handbook. The book will take you to the data science world using Python as a media and will take you beyond the limit you have imagined before.
Table of Contents
 IPython Beyond Normal Python
 Introduction to NumPy
 Data Manipulation with Pandas
 Visualization with Matplotlib
 Machine Learning
14. R Programming for Data Science
R is an essential programming language that is used for statistical computations, representation in the graph, and data analysis. So, as a learner of data science, R programming is a must, and it’s a vast subject. To make it easy and fruitful one, R programming for Data Science book is written. Plenty of necessary and essential topics are discussed in the book.
Table of Contents
 History and overview of R
 Getting Started with R
 R Nuts and Blots
 Getting Data In and Out of R
 Using Textual and Binary Romans for Storing Data
 Interfaces to the Outside World
 Subsetttinig R Objectives
 Necrotised Operations
 Dates and Times
 Managing Data Frames with the dplyr Package
 Control Structures
 etc.
15. Malware Data Science: Attack Detection and Attribution
Where it is good, there is a threat. Data science is no exception to having threats being good. Thereby data science books and big data books also project some risk factors in their contents. But, this is the book that is completely written about threats to data science. The book nicely introduces the threats to data science and then shows the ways to get rid of them. There are different detectors, tools, and many more, which the book discusses nicely.
Table of Contents
 Basic Static Malware Analysis
 Beyond Basic Static Analysis: x86 Disassembly
 A Brief Introduction to Dynamic Analysis
 Identifying Attack Campaigns Using Malware Networks
 Shared Code Analysis
 Understanding Maxine LearningBased Malware Detection System
 Building Machine Learning Detectors
 Visualizing Malware Trends
 Deep Learning Basics
 Building Neural Network Malware Detector with Kiera’s
 Becoming a Data Scientist
16. Practical Statistics for Data Scientists
Data scientists are the mentors, moderators, developers, and guardians of data science. A lot of statistics are required for data scientists, and they must know how to manage and process those. O’Reilly collections have another data science book that covers all the statistical requirements that a data scientist may require. The book classifies all the data process, teaches data analysis, teaches the distribution process of data, and many more.
Table of Contents
 Exploratory Data Analysis
 Data Sampling Distributions
 Statistical Experiments and Significance Testing
 Regression and Prediction
 Classification
 Statistical Machine Learning
 Unsupervised Learning
17. Probability and Statistics for Data Science
Probability and Statistics are two very essential elements to complete data science. There are a lot of important topics like algebra, regression, etc. which play a very important role in learning data science. This data science book discusses all these important topics in detail and fulfills the expectation of the readers. Some basic and essential topics like Bayesian statistics, Random variable, Hypothesis testing, etc. are nicely discussed in the book.
Table of Contents
 Basic Probability Theory
 Random Variable
 Multivariate Random Variables
 Expectation
 Random Processes
 The converse of Random Processes
 Markov Chains
 Descriptive Statistics
 Frequent its Statistics
 Bayesian Statistics
 Hypothesis Testing
 Linear Regression
 Set Theory
 Linear Algebra
18. The Data Engineering Cookbook: Mastering the Plumbing of Data Science
The book introduces the concept of data engineers and data scientists. At the very beginning, the book will teach you about the way to learn code and introduce it with Github. The very famous and dominating kernel named Linux is one of the main points of discussion in the book.
Table of Contents
 Data Engineer vs. Data Scientists
 Learn to Code
 Get Familiar with Github
 Learn How a Computer Works
 Computer Networking Data Transmission
 Security and Privacy
 Linux
 The Cloud
 Security Zone Design
 Big Data
 Data Warehouse vs. Data Lake
 Hadoop Platforms
 Is ETL Still Relevant for Analytics?
 Docker
 REST APIs
 Databases
 Data Processing
 Apache Kafka
 Data Visualization
 Building a Data Platform Example
19. Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence
Statistics with Julia: fundamentals for Data Science, Machine Learning, and Artificial Intelligence is a very good book that covers not only Data Science but also machine learning and artificial intelligence. The book is aimed to help the research of prediction, analyzing, programming, designing, planning, etc. With many essential topics, the book contains a good list of codes for the learners.
Table of Contents
 Introducing Julia
 Basic Probability
 Probability Distributions
 Processing and Summarizing Data
 Confidence Intervals
 Hypothesis Testing
 Linear Regression and Extensions
 Machine Learning Basics
 Simulation of Dynamic Models
20. The Data Science Design Manual
The author of the book ‘The Algorithm Design Manual’ now presents you with another fabulous book named ‘The Data Science Design Manual.’ The book proves that data science is not rocket science rather an easy topic. It teaches the process of developing mathematical intuition. After reading the book, you can act like you are a good Statistician. The book is a great piece for both students and instructors in data science.
Table of Contents
 What is Data Science
 Mathematical Preliminaries
 Data Munging
 Scores and Rankings
 Statistical Analysis
 Visualizing Data
 Linear and Logistic Regression
 Distance and Logistic Methods
 Machine Learning
 Big Data: Achieving Scale
 Coda
The Ending Remarks
Data Science is like a chain reaction. It creates the created things. The usage area of Data Science is enormous. It is mostly used in big business purposes where an important decision is taken from basing on many data. We have tried to gather different categories of data science and big data books. We believe these books will feed knowledge to the newbies and the advanced level readers. All the books are very good for the instructors to use them in their teaching process.
Finally, we conclude with the hope that the article has helped you in finding your desired data science and big data books. Please share it with your friends. Enlighten us with your ideas and books, which could be included here.
Thanks for such a wonderful information
I will honestly say that this is an impressive collection of information and guides. Well done to the authors. Nothing but study. I really respect your work and effort in creating this whole list. Data science for everyone!