Best Data Science Books

This practical book is perfect for programmers interested in delving into the exciting field of machine learning. With concrete examples, minimal theory, and Python frameworks like Scikit-Learn and TensorFlow, the author shows readers how to build intelligent systems capable of learning from data. From simple linear regression to deep neural networks, you'll gain an intuitive understanding of machine learning techniques and architectures while getting hands-on experience through exercises in each chapter. Dive into the machine learning landscape and start building intelligent systems today.

This book explores the fascinating world of deep learning, which teaches computers to understand the world through a hierarchy of concepts. It covers mathematical and conceptual background, techniques used in industry, and research perspectives. Readers will learn about relevant topics in linear algebra, probability theory, and more, as well as practical applications in areas like natural language processing and computer vision. Perfect for students or engineers looking to incorporate deep learning into their work. Supplementary material available on the website.

This updated book on data science teaches you the tools, algorithms, and principles underlying data science. With a focus on Python 3.6, author Joel Grus shows you how to implement these tools from scratch. Whether you have a background in programming or mathematics, this book will help you master the fundamentals of data science such as statistics, probability, and machine learning. The book also covers newer topics like deep learning and natural language processing. With practical examples and exercises, you'll be able to clean, manipulate, and analyze data, and implement models like neural networks and decision trees.

Explore the revolutionary science of big data, which allows us to analyze vast amounts of information and draw surprising conclusions. Discover how this emerging technology will dramatically impact the economy, science, and society as a whole. From predicting the spread of diseases to identifying dangerous manholes, big data has the power to transform the way we think about business, health, politics, education, and innovation. However, with this power comes fresh threats, from the end of privacy to the possibility of being penalized for things we haven't even done yet. Discover what you can do to protect yourself from these hazards in this brilliantly clear and surprising work by leading experts in the field.

Learn practical ways to build your own machine learning solutions using Python with this book! Perfect for beginners, authors Andreas Müller and Sarah Guido will guide you through the process of creating successful machine-learning applications with the scikit-learn library. You'll discover how to use machine learning algorithms and focus on the practical aspects of the process. Don't wait to unlock the full potential of machine learning with Python!

Explore the fundamental principles of data science with Data Science for Business. This book is written by experts Foster Provost and Tom Fawcett, and teaches readers the "data-analytic thinking" needed to extract valuable business insights from collected data. With the use of real-world examples, you'll learn how to improve communication between stakeholders and data scientists, as well as how to participate intelligently in data science projects. Discover how data science can support business decision-making and gain a competitive advantage.

Discover the exciting world of statistics and its real-world applications in Naked Statistics. In this engaging book, Charles Wheelan simplifies complex concepts and shows us how data and statistics can be used to answer pressing questions, from cheating schools to rising autism rates. With insightful examples, including Schlitz Beer marketers and Let's Make a Deal, Wheelan brings statistics to life and strips away the technical details to focus on intuition. For those who struggled through Stats 101 or anyone curious about the power of data, Naked Statistics is a must-read.

An essential guide for anyone looking to manipulate, clean, and analyze structured data using Python. This second edition introduces practical case studies and covers important libraries like NumPy and pandas, offering helpful tips for beginners and experienced programmers alike. Written by the creator of the pandas library, this book is a must-read for anyone involved in data-intensive applications with Python.

This book is a must-have for aspiring data scientists who lack formal statistical training. It explores statistical methods from a data science perspective and provides practical guidance on their application. The second edition includes comprehensive examples in Python and advice on avoiding their misuse. You'll learn all about exploratory data analysis, random sampling, experimental design, regression, and more. Plus, the book covers unsupervised learning methods for extracting meaning from unlabeled data. If you're familiar with R or Python and have some exposure to statistics, this accessible reference will bridge the gap for you.

This machine learning book covers everything modern machine learning has to offer and can be read in just one week. It's concise and up to date, written by an experienced practitioner. Plus, it has a continuously updated wiki with additional resources. With flexible pricing and formats, you can choose what suits you best, and you can even read the book chapters for free before deciding whether to buy.