Top Data science books you should definitely read

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Data Science is one of the highest-paid and most popular fields nowadays. And there is nothing better than reading data science books to get the ball rolling. Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning, and much more.

Top Data science books you should definitely read

1. Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce, Andrew Bruce 

This book gives you a good overview of all the concepts that you need to learn to master data science. The book is not too detailed but gives good enough information about all the high-level concepts like randomization, sampling, distribution, sample bias, etc. Each of these concepts is explained well and there are examples along with an explanation of how the concepts are relevant in data science. 

Important note: It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed.

With this book, you’ll learn why exploratory data analysis is a key preliminary step in data science, how random sampling can reduce bias and yield a higher quality dataset, even with big data, how the principles of experimental design yield definitive answers to questions, how to use regression to estimate outcomes and detect anomalies, key classification techniques for predicting which categories a record belongs to, statistical machine learning methods that “learn” from data, unsupervised learning methods for extracting meaning from unlabeled data.

2. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data by EMC Education Services

The whole data analytics lifecycle is explained in detail along with case study and appealing visuals so that you can see the practical working of the entire system. You can easily understand the entire big picture of how analytics is done as each step is like one chapter in the book. The book includes clustering, regression, association rules and much more along with simple, everyday examples that one can relate to. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment.

This book will help you become a contributor on a data science team, deploy a structured lifecycle approach to data analytics problems, apply appropriate analytic techniques and tools to analyzing big data, learn how to tell a compelling story with data to drive business action. 

3. Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Anything told as a story and shown as graphics fit into our mind easily and stays there permanently. The book is quite impactful and deals with the fundamental concepts of data visualization for you to understand how to make the most of the huge chunks of data available in the real world. The author’s way of explaining every concept is totally unique as he tells it in the form of a compelling story. You wouldn’t even realize how many concepts you can grasp in a day of reading the book – getting to know the context and audience, using the right graph for the right situation, recognizing and removing the clutter to get only the important information, utilize the most significant parts of the data and present them to users. You’ll learn how to understand the importance of context and audience, determine the appropriate type of graph for your situation, recognize and eliminate the clutter clouding your information, direct your audience’s attention to the most important parts of your data, think like a designer and utilize concepts of design in data visualization and leverage the power of storytelling to help your message resonate with your audience 

4. The data science handbook by Field Cady

It is not a purely technical book but a quick reference as it contains information in the form of questions and answers from various leading data scientists. The questions flow in an organized manner and help you understand each aspect of data science like data preparation, the importance of big data, the process of automation and how data science is the future of the digital world. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features extensive sample code and tutorials, core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems, coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity, wide variety of case studies from industry and practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed. 

5. Business Analytics: The Science Of Data – Driven Decision Making by Dinesh Kumar

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This is an awesome in-depth book that explains the theory as well as practical applications to give wholesome knowledge. The author approaches the topics with subtlety and presents many case studies that are easy to understand, comprehend and follow. The book has everything from economics, statistics, finance and all you need to start learning data science. The book has been written with a lot of effort and experience and the way insights have been presented shows the same. It includes statistical and analytical tools, machine learning techniques and amalgamates basic and high-level concepts very well. You will also learn about scholastic models and six sigma towards the end of the book.

6. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Michael J. A. Berry,  Gordon S. Linoff  

A wonderful book that explains data mining from scratch. It starts with explaining about the digital age, data mining and then moves to explain the kinds of data that can be mined, the patterns that can be mined, for example, cluster analysis, predictive analysis, correlations, etc., and the technologies that are used – statistics, machine learning, and database. It has a lot of basic and advanced techniques for classification, cluster analysis and also talks about the trends and on-going research in the field of data mining.In addition, this book covers more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Also, it touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more. 

7. Thinking with Data: How to Turn Information into Insights by Max Shron

It provides a lot of useful insights and enables critical business thinking in the reader. It helps you relate to why things are happening the way they are. Through the chapters, you will learn how to ask good meaningful questions, note down the important details of an idea and get key information to focus on. It nicely covers data-specific patterns of reasoning. The book will help you think ‘why’ and not just ‘how’. It covers what is called as CoNVO – context, needs, vision, and outcome. Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved.

This book will help you understand how to pin down the details of an idea, receive feedback, and begin prototyping. 

8. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster

The author has done an exceptional job in penning all the concepts in the form of stories that are easy to comprehend. Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.

9. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett

The book emphasizes on discovering new business cases rather than just processing and analyzing data. Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Also, it will allow you to understand how data science fits in your organization—and how you can use it for competitive advantage. It will show you how to treat data as a business asset that requires careful investment if you’re to gain real value. 

10. Designing data-intensive applications by Martin Kleppmann

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This book helps understand the architecture of today’s data systems and how they can be fit into applications that are data-driven and data-intensive. The author discusses various aspects of designing database and data solutions and gives loads of other resources too for you to further your knowledge on the topic. 

11. The Art of Data Science by Roger Peng and Elizabeth Matsui

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

12. Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil  and Rachel Schutt

A collection of talks from data scientists working at a variety of different companies that’s meant to cut through the hype and help you understand how data science works in the real world. These experts not only offer knowledgeable lectures on the subject but also share relevant case studies and code, diving into accessible examples. It covers algorithms, methods, models, and data visualization, acting as a practical go-to technical resource, statistical inference, exploratory data analysis, and the data science process, spam filters, Naive Bayes, and data wrangling, logistic regression, financial modeling, recommendation engines and causality, data visualization, social networks and data journalism, data engineering, etc. 

13. Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners by Dursun Delen

Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. 

Delen’s holistic approach covers key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies – including lessons from failed projects. It’s all designed to help you gain a practical understanding you can apply for profit. This book will show you how to leverage knowledge extracted via data mining to make smarter decisions, how to use standardized processes and workflows to make more trustworthy predictions, how to predict discrete outcomes, numeric values, and changes over time. Also it will show you how predictive algorithms can be drawn from traditional statistics and advanced machine learning. You will discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection. 

14. Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals by Field Cady  

Accomplished data scientist and author Field Cady describes both the “business side” of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.

15. The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science by Alex Gorelik 

The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book.

16. Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginners by Herbert Jones 

Data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers. In this guidebook, you will find the following topics: how to do an explorative data analysis, data mining, machine learning algorithms, data modeling, data visualization, how to use data science to help your business grow. 

17. Analytics: Data Science, Data Analysis and Predictive Analytics for Business by Daniel Covington

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This book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time. The contents of this book are designed to help you use data to your advantage to enhance business outcomes. Also, you will learn which steps to take in performing predictive analysis, what techniques you need to employ to achieve sustainable success, regression techniques, machine learning strategies and risk management tips. 

18. The Chief Data Officer Handbook for Data Governance by Sunil Soares 

In this book, Sunil Soares provides a practical guide for today’s chief data officers to manage data as an asset while delivering the trusted data required to power business initiatives, from the tactical to the transformative. The guide describes the relationship between the CDO and the data governance team, whose task is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. 

19. Discovering Knowledge in Data: An Introduction to Data Mining by Daniel T. Larose and Chantal D. Larose 

This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and explains the most current data science methods and techniques. 

20. The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists by Carl Shan, William Chen, Henry Wang, Max Song 

The Data Science Handbook contains candid interviews with 25 of the world’s best data scientists. You will find in-depth conversations about their careers, personal stories, perspectives on data science and life advice.   

21. Managing Data Science: Effective strategies to manage data science projects and build a sustainable team by Kirill Dubovikov  

Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. In this book, you’ll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you’ll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you’ll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you’ll encounter on a daily basis.

22. Principles of Data Science: Learn the techniques and math you need to start making sense of your data by Sinan Ozdemir  

With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

I hope you found these data science books useful and if you need any help with your data science projects, you can always count on us!  


  • Ekaterina Novoseltseva

    Ekaterina Novoseltseva is an experienced CMO and Board Director. Professor in prestigious Business Schools in Barcelona. Teaching about digital business design. Right now Ekaterina is a CMO at Apiumhub - software development hub based in Barcelona and organiser of Global Software Architecture Summit. Ekaterina is proud of having done software projects for companies like Tous, Inditex, Mango, Etnia, Adidas and many others. Ekaterina was taking active part in the Apiumhub office opening in Paseo de Gracia and in helping companies like Bitpanda open their tech hubs in Barcelona.

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