Here is our list of the best data science books.
Data science books are books that provide techniques and insights to help readers understand the different aspects of data science. Examples of data science books include The Data Warehouse Toolkit by Ralph Kimball and Margy Ross and Building Machine Learning Powered Applications: Going from Idea to Product by Emmanuel Ameisen. The purpose of data science books is to help readers acquire the necessary skills in all fields of data science, such as data cleaning, data interpretation, and data modeling.
This list includes:
- data science management books
- data science books for beginners
- advanced data science books
- books about data science
Let’s get started!
List of data science books
Data science books provide valuable knowledge and resources to individuals seeking to learn about data science and its application. Here is a list of data science books you should consider reading.
1. The Data Warehouse Toolkit by Ralph Kimball and Margy Ross
A key part of modern data management is data warehousing. The Data Warehouse Toolkit offers a complete guide for creating and designing data warehouses. This book covers everything from the basics to advanced methods. Written by experts Ralph Kimball and Margy Ross, the book takes a practical approach, using real-life examples to explain all the techniques and concepts. Whether you are technical or not, if you are interested in data warehousing, then this book is for you.
Notable quote: ”Simplicity is the fundamental key that allows users to easily understand databases and software to efficiently navigate databases.”
Check out The Data Warehouse Toolkit.
2. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett
Within this book, accomplished data science authorities Foster Provost and Tom Fawcett present the essential tenets of data science. Data Science for Business delves into the analytical mindset necessary to extract business value from data accumulation. The book unpacks tangible instances of real-world business challenges. Moreover, the authors aid readers in enhancing communication between data scientists and business stakeholders during collaborative data science endeavors. Drawing upon the proficiency of Provost and Fawcett, readers will glean diverse approaches to applying data science to making informed business choices.
Notable quote: ”It is important to understand data science even if you never intend to apply it yourself.”
Check out Data Science for Business.
3. Building Machine Learning Powered Applications: Going from Idea to Product by Emmanuel Ameisen
Building Machine Learning Powered Applications stands as a leader in the realm of data science management books. Emmanuel Ameisen, the author, is a data scientist dedicated to the tangible utilization of machine learning. The book places a spotlight on the pragmatic use of this captivating facet of data science that boasts a multitude of practical applications. This guide encompasses a thorough pathway to crafting applications that harness the prowess of machine learning. A notable forte of this work is the author’s emphasis on the hands-on deployment of machine learning for real-world scenarios.
Ameisen adeptly illustrates the methods to preprocess data for optimal suitability in machine learning endeavors. For instance, readers will learn about the implementation of feature scaling, feature engineering, and data cleaning, which are essential for constructing models that are both robust and precise. Furthermore, the book delves into the deployment of machine learning models, tapping into popular frameworks like Docker and Flask. This volume proves accessible to novices while also offering insights for seasoned machine learning practitioners.
Notable quote: ”Machine learning is powerful and can unlock entirely new products, but since it is based on pattern recognition, it introduces a level of uncertainty.”
Check out Building Machine Learning Powered Applications.
4. Data Science from Scratch: First Principles with Python by Joel Grus
Utilizing the Python programming language, Data Science from Scratch presents a pragmatic look into the realm of data science. This guide spans the terrain of data cleaning, machine learning, deep learning, and data science itself. By centering around tangible data science predicaments, the book acquaints readers with pivotal ideas through hands-on implementations achieved via coding and statistical methods. While possessing a grasp of Python is not mandatory to derive benefits from this volume, a foundational understanding of the subject matter can undoubtedly facilitate the learning process.
For beginners entering the field without practical exposure to ethical considerations, Data Science from Scratch dedicates a chapter to the ethical facets of data science. Additionally, the book introduces readers to the rudiments of statistics, probability, and linear algebra before delving into more intricate topics like deep learning and machine learning.
Notable quote: ”Data science is a hot and growing field, and it doesn’t take a great deal of sleuthing to find analysts breathing prognosticating that over the next 10 years, we’ll need billions and billions more data scientists than we currently have.”
Check out Data Science from Scratch.
5. Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo
Annalyn Ng and Kenneth Soo deliver a clear introduction to the realm of data science, even for individuals without a background in statistics or mathematics. By employing straightforward and commonplace illustrations, Ng and Soo demystify intricate subjects. Thus, the authors cater to readers unfamiliar with the subject matter. Furthermore, Numsense! Data Science for the Layman underscores the significance of teamwork and effective communication within the domain of data science. Crafting sound decisions rooted in data necessitates the involvement of diverse stakeholders. Therefore, the authors offer ample direction on the art of conveying data-derived insights to fellow collaborators.
Notable quote: ”To appreciate how data science is driving the present data revolution, there is a need for the uninitiated to gain a better understanding of this field.”
Check out Numsense! Data Science for the Layman: No Math Added.
6. The Art of Data Science: A Guide for Anyone Who Works with Data by Roger Peng and Elizabeth Matsui
In The Art of Data Science, authors Roger Peng and Elizabeth Matsui draw from their experiences to guide both managers and beginners in understanding data science. With expertise in data and analysis management, the authors offer practical advice for achieving successful results and avoiding common mistakes. The book emphasizes the human aspect of data science, highlighting the need for technical skills, problem-solving, communication, and collaboration. Additionally, lessons cover practical insights for approaching data science from a people-centered perspective. Readers will also learn how to understand stakeholders’ goals and needs and how to build credibility and trust throughout the process.
Notable quote: ”Data analysis is hard, and part of the problem is that few people can explain how to do it.”
Check out The Art of Data Science.
7. Data Science for Dummies by Lillian Pierson
Lillian Pierson has a knack for simplifying intricate topics, and her work Data Science for Dummies exemplifies this skill. Focusing on the business facets of data science, the book serves as an introductory handbook for aspiring professionals entering the field. Regarded as one of the top data science books for newcomers, it teaches readers the fundamentals of big data and the practical application of data science in daily life. Moreover, the book delves into machine learning, artificial intelligence, data visualization methods, data engineering, and algorithms.
Pierson offers an overview of data visualization tools and techniques, highlighting their ability to effectively convey data insights. Data Science for Dummies serves as both a beginner’s introduction to the field and a useful reference for professionals aiming to enhance their expertise.
Notable quote: ”Data science and artificial intelligence have disrupted the business world so radically that it’s nearly unrecognizable compared to what things were like just 10 or 15 years ago.”
Check out Data Science for Dummies.
8. Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support by Ramesh Sharda, Dursun Delen, Efraim Turban, and David King
This textbook covers different topics related to analytics, data science, and artificial intelligence. Analytics, Data Science, and Artificial Intelligence is one of the top advanced data science books that explores how these areas connect within organizations and how decisions impact the whole system. The authors also talk about ethical considerations in data science and offer guidance on handling decision-making issues. The book includes various case studies that show how technologies can improve system performance. Overall, this book is a valuable read for professionals and students looking to develop skills in different areas of data science and management.
Notable quote: ”Analytics has become the technology driver of this decade.”
Check out Analytics, Data Science, and Artificial Intelligence.
9. Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schönberger and Kenneth Cukier
Viktor Mayer-Schönberger and Kenneth Cukier delve into the far-reaching impact of data across various facets of human life. Their writing spans business, personal affairs, government, and scientific fields. Big Data centers on the capacity of algorithms to unveil insights about individuals through online behavior analysis. For instance, online retailers predict purchasing trends based on browsing habits, while dating apps employ data to shape romantic journeys. This book serves as a valuable resource in the realm of management science, offering readers insight into the potential of big data and how it fuels growth and innovation. For executives and managers aiming to construct a successful data strategy, Big Data is an ideal entry point.
Notable quote: ”Big data refers to our newfound ability to crunch a vast quantity of information, analyze it instantly, and draw sometimes astonishing conclusions from it.”
Check out Big Data.
10. Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil and Rachel Schutt
Doing Data Science is a practical guide that offers a thorough introduction to data science, covering concepts, tools, and techniques used in the industry. Written by Cathy O’Neil and Rachel Schutt, this book is great for beginners exploring data science’s applications in business. With a focus on real-world examples, it shows how data science can solve complex business problems. O’Neil and Schutt provide practical advice on approaching data science projects, including defining problems, collecting and cleaning data, and developing and testing models.
Notable quote: ”The world is opening up with possibilities for people who are quantitatively minded and interested in putting their brains to solve the world’s problems.”
Check out Doing Data Science.
11. Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics by Thomas Nield
Essential Math for Data Science helps readers master crucial statistics, machine learning, and data science proficiencies. Within this book, Thomas Nield expertly steers readers through subjects including linear algebra, probability, logistic regression, calculus, and neural networks. Moreover, Nield imparts practical wisdom regarding the landscape of data science and how to leverage these insights for maximizing a career in this field. Essential Math for Data Science serves as your guiding companion on your journey to acquire data science expertise, all the while sidestepping common mistakes and biases.
Notable quote: ”Number theory goes all the way back to ancient times when mathematicians studied different number systems, and it explains why we have accepted them the way we do today.”
Check out Essential Math for Data Science.
12. Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning by Alex J. Gutman and Jordan Goldmeier
In Becoming a Data Head, authors Alex J. Gutman and Jordan Goldmeier delve deeply into the realm of data science, explaining the essential tools for thriving within this domain. Gutman and Goldmeier equip readers with the skills to ask relevant questions about statistics and outcomes in a professional setting, understand the nuances of machine learning, and steer clear of common pitfalls in data interpretation. The book delves into various subjects, including data visualization, machine learning algorithms, preprocessing, and statistical analysis. Considered one of the top data science books for beginners, Becoming a Data Head provides an invaluable resource for those starting their data science journey.
Notable quote: ”To become better at understanding and working with data, you will need to be open to learning seemingly complicated data concepts.”
Check out Becoming a Data Head.
13. Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy by Oliver Theobald
In Data Analytics for Absolute Beginners, you will learn the basic algorithms that help you think like a data scientist. Author Oliver Theobald uses a step-by-step approach, similar to building with Lego blocks, to gradually build your knowledge. This book takes you from having no knowledge to becoming skilled at analyzing and discussing data problems. Theobald uses practical and visual examples to guide you through the concepts. You will understand when and how to use techniques like classification clustering, natural language processing, and regression analysis. Additionally, you will learn how to make smarter business decisions using data visualization.
Notable quote: ”Data takes the form of everything from words in books to sales logged in spreadsheets, as well as text and images contained in social media posts.”
Check out Data Analytics for Absolute Beginners.
Conclusion
Data science is one of the highest-paying fields of data, and this field will continue to thrive and innovate. The best way to stay on top of your game is by reading books about data science. Reading these books will provide a holistic view of the field. Contrary to what some might believe, data science is open to more than just computing. Data science also includes programming, machine learning, statistics, probability, and mathematics. Thriving in these fields requires adequate knowledge, and data science books will equip you with this information.
Although several books are available on data science, you do not have to read them all. The authors designed these books to appeal to different audiences, from beginners to advanced. You should read a book that appeals to your current experience level and build up from there.
Grace He