Best Data Science Books Available online

The 20 Best Data Science Books Available online in 2020-21


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 End-User

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 decision-making 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
  • K-Nearest 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, E-Commerce, 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 command-line are given.

Table of Contents

  • What is the Command Line
  • Getting Started
  • Obtaining Data
  • Getting Reusable Command-Line Tools
  • Scrubbing Data
  • Managing Your Data Workflow
  • Exploring Data
  • Parallel Pipelines
  • Modeling Data
  • List of Command-Line 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 far-reaching 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 in-depth 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 well-known 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 High-Tech 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 the 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 k-means 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 Learning-Based 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
  • 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

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.