Here’s a full list of Data Science Books with their authors, publishers, and an unbiased review of books as well as links to the Amazon website to directly purchase these books.
 Data Science Books for Beginners
 Data Science Books for Intermediates
 Data Management
 Popular Data Science Books
 Data Science Resources
 Additional Recommendation
1. Data Science Books for Beginners
1. “Python for Data Science for Dummies” by John Paul Mueller and Luca Massaron  
“Python for Data Science for Dummies” book teaches the reader how to use Python programming to acquire, organize, process, and analyze large amounts of information. It also focuses on using basic statistics concepts to identify trends and patterns. The readers will learn python development, how to manipulate data, and design compelling visualizations. By the end of the book, they will be able to solve scientific computing challenges. The book also explains objects, functions, modules, and libraries and their role in data analysis. The book is useful for anyone interested in learning about data analysis and Python.


2. “Agile Data Science” by Russell Jurney  
“Agile Data Science” book gives a handson guide to u recent data scientists to learn how to use the agile data science development methodology to build various data applications with Python, Apache spark, Kafka, and other tools. It illustrates how to build a data platform for building, deploying and refining analytics applications with Apache kafka, MongoDB, ElasticSearch, d3.js, scikitlearn, and Apache Airflow. It helps to learn a different approach that lets quick change in data analysis.


3. “Data Science for Business: What You Need to Know about Data Mining and DataAnalytic Thinking” by Foster Provost  
“Data Science for Business: What You Need to Know about Data Mining and DataAnalytic Thinking” book introduces the basic principles of data science and guides us to the “dataanalytic thinking” needed to extract useful information and the business value from the data you collect. This guide also helps to understand many of the data mining techniques used today. It is based on an MBA course that is taught at New York University. This book provides various examples of realworld business problems to describe data science concepts. It helps to learn how to upgrade communication between stakeholders and data scientists. It also explores how to think about data analysis, and fully understand how data science methods can support business decisions. This book also helps beginners and intermediate professionals who want to learn data analytics. The book provides a description on mathematical functions for fitting a model for the data. Topics like visualization of model performance, evidence and probabilities have been discussed in detail. Each chapter ends with the chapter summary which clears understanding.


4. “Data Science for Dummies” by Lillian Pierson  
“Data Science for Dummies” book is for students and professionals. This book is divided into six parts and twenty three chapters in total. The Part 1 gives a basic introduction of data science engineering and application of data driven insights to business and industry. The Part 2 provides description on machine learning, statistical modelling building of models that operates on Internetofthings devices. The Part 3 gives the information on data visualization designs and its web based applications. The Part 4 gives information on using Python, R, SQL, Excel, and Knime for data science. The Part 5 gives a description of solving world problems using data science. The Part 6 contains information on Open Data resources, and data science tools and its applications. This book gives theoretical as well as practical knowledge on data science.


1. “Data Smart: Using Data Science to Transform Information into Insight” by John W Foreman  
“Data Smart: Using Data Science to Transform Information into Insight” helps those students who are serious about studying the analytic techniques, the math, and the behind big data. Each chapter covers a different technique in spreadsheet including mathematical optimization, nonlinear programming and various genetic algorithms. It demonstrates the clustering using kmeans, spherical kmeans, and graph modularity. It illustrates data mining tin graphs, such as outlier detection and also discusses supervised AI through logistic regression models. It also helps to move data from spreadsheets into the R programming language.


2. “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die” by Eric Siegel  
“Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die” is easily understandable and simple for tech geeks as well as the common man consisting of the latest case studies and various highend computing techniques. It tries to answer all the questions about situations that can be confirmed but can still be predicted in advance.


3. “Doing Data Science: Straight Talk from the Frontline” by Cathy O’Neil  
“Doing Data Science: Straight Talk from the Frontline” is based on Columbia University’s introduction to data science class which tells everything to know about data science. The book provides many chapterlong lectures which were given by data scientists from big companies such as Google, Microsoft, and eBay on new algorithms, methods, and models by illustrating case studies and the code they use. If we have prior knowledge of linear algebra, probability, and statistics, and have programming experience, this book is an ideal book for introduction to data science. It covers topics including statistical inference, data science process, algorithms, data visualization and data engineering. It also demonstrates the concepts such as Naive Bayes, linear regression, financial modelling and Hadoop Mapreduce.


4. “Machine Learning for Big Data: HandsOn for Developers and Technical Professionals” by Jason Bell  
“Machine Learning for Big Data: HandsOn for Developers and Technical Professionals” book gives an overview of various techniques used to gain insight from data. It gives some practical explanations on how the code is put together. It shows how one can apply the right machine learning techniques to own problems. It explores how data can be powerful and at the same time how this power can be used against us. providing coded solutions for realworld examples. There is a strong focus on data preparation and data cleaning, the core fundamentals of machine learning. Each chapter includes how the code works and running examples.


5. “Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data” by EMC Education Services  
“Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data” book offers an overview of big data technologies and explains what is needed to succeed with big data. It gives examples of both successful and failed data practices undertaken by startups, online firms and large companies. It provides a comprehensive overview of visualizing and presenting the data in a more reasonable manner. It also discusses in detail the discovering and analyzing of data science and big data analytics. The book is useful for students, researchers and data analysts.
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3. Data Management
1. “Data Management” by Richard Watson
“Data Management” Book Review: This book is useful for students and professionals who want to make their career in data analysis. This book discusses basic concepts of database design and management of databases. This book gives a detailed description of data modelling and SQL. Topics like R, data visualization, and text mining have been explained in depth in the book. Also, detailed information is provided on Hadoop distributed file system and MapReduce. This book contains discussions on data warehousing, data mining, OLAP and multidimensional databases. This book contains exercises that increase theoretical as well as practical knowledge. This book is helpful for carrying out researchbased work in the area of data science.


2. “Principles of Data Management – Facilitating Information Sharing” by Keith Gordon
“Principles of Data Management – Facilitating Information Sharing” Book Review: This book is useful for professionals in the areas ranging from business analysis to web development. This book explains the basics and applications of database management in a clear and concise manner. Data quality and Corporate data modelling have been discussed thoroughly. This book gives a description on implementation of data management functions from a successful business point of view. This book provides information on relation between data and database administrators, system development teams and business users. This book also contains the information on the technical issues faced by database management professionals. This book is aimed for professionals who are in the areas of database management, business analysis, and IT.


3. “The DAMA Guide to the Data Management Body of Knowledge” by DAMA International
“The DAMA Guide to the Data Management Body of Knowledge” Book Review: This book is for professionals making their career in the data framework and its management. This book starts with basic concepts, terminologies and definitions on data management functions. This book provides detailed description on data governance, data architecture management, and data development. Topics like database operations management, data security management and Reference & master data management have been covered deeply in the book. This book also gives information on data warehousing & business intelligence management, data quality management and professional development. The detailed explanation of topics like document & content management and metadata management makes this book very helpful for researchers working in the area of database management.


4. “Master Data Management and Data Governance” by Alex Berson
“Master Data Management and Data Governance” Book Review: This book is useful for students and professionals in the area of data management and data governance. This book is divided into five major parts. The Part 1 contains the basic introduction on Master Data Management (MDM) and its applications by industry. The Part 2 contains the architecture, database management and modelling of MDM. The Part 3 contains data security, privacy and regulatory compliances for Master Data. The Part 4 contains implementation and governance of Master Data Management. The Part 5 contains markets, trends, and direction in relation to MDM. This book contains appendices which contain a list of acronyms and glossaries. Each chapter contains a list of references. This book is good for professionals in business areas.


5. “Data Management for Researchers: Organize, Maintain and Share your Data” by Kristin Briney
“Data Management for Researchers: Organize, Maintain and Share your Data” Book Review: This book is mainly for the researchers working in the area of data management. This book contains eleven chapters with references and indexes at the end of the book. This book begins with a basic introduction on database management and problems related to it. The book discusses the data lifecycle and the data roadmap. This book provides information on planning and creating database management plans, and data policies. This book gives documentation along research notes and lab notebooks. Information on file organization, storage and backup of data have been presented in detail in the book. Sharing of data, data reuse and management of sensitive data have been discussed in detail. Each chapter ends with the chapter summary, which helps in better understanding and revision.


6. “Data Management Using Stata: A Practical Handbook” by Michael N Mitchell
“Data Management Using Stata: A Practical Handbook” Book Review: This book is for beginners in the field of Stata. This book contains information on the relation between raw data and statistical analysis. This book contains detailed information on Stata Graphics, Data Management Using Stata, and Visualizing and Interpreting Regression Models using Stata. This book also provides information on Stata for the Behavioral Sciences. This book contains examples which help in understanding. This book serves its purpose for research works.


7. “Big Data: Principles and best practices of scalable realtime data systems” by Nathan Marz and James Warren
“Big Data: Principles and best practices of scalable realtime data systems” Book Review: This book is for professionals in data architecture. This book contains three parts. THe Part 1 contains the information on data models for big data along with architecture and implementation. The Part 2 discusses serving layers and illustrations related to it. The Part 3 contains real time views, Queuing and stream processing, and Microbatch stream processing as well as their illustrations. The book gives an indepth knowledge on lambda architecture. Introduction to big data systems. Information on tools like Hadoop, Cassandra, and Storm and Extensions to traditional database skills are given in this book. Each chapter has practice exercises.


8. “Clinical Analytics and Data Management for the DNP” by Martha L Sylvia PhD MBA RN and Mary F Terhaar DNSc RN
“Clinical Analytics and Data Management for the DNP” Book Review: This book is for DNP students. This book gives information on the complete process of data management, including planning, and data collection. This book discusses data governance and cleansing, analysis, and data presentation. The book provides examples of techniques using SPSSAE software. This book provides practical information by presenting content on DNP innovations and projects. Each chapter contains objective questions, references and examples which is helpful for understanding purposes.


4. Popular Data Science Books
1. Practical Statistics for Data Scientists Book
2. Statistics for Data Scientists Book
3. Practical Data Science with R Book
4. Python for Data Analysis Book
5. Data Science from Scratch Book by Joel Grus
6. Mathematics for Data Science Book
You can buy these additional reference books on Data Science from “Amazon USA” OR “Amazon India”.
5. Data Science Resources
1. Data Science MCQs
2. Data Science Tests
3. Data Science Certification Contest
4. Data Science Internship
6. Additional Recommendation
1. Data Management Books
2. Data Analytics and Visualization Books
3. Educational Data Analytics Books
4. Computation and Data Analysis Books
5. Data Mining and Data Warehousing Books