Reflecting the interdisciplinary nature of the field, this new book series brings together researchers, practitioners, and instructors from statistics, computer science, machine learning, and analytics. The series will publish cutting-edge research, industry applications, and textbooks in data science.

**Features:**

- Presents the latest research and applications in the field, including new statistical and computational techniques
- Covers a broad range of interdisciplinary topics
- Provides guidance on the use of software for data science, including R, Python, and Julia
- Includes both introductory and advanced material for students and professionals
- Presents concepts while assuming minimal theoretical background

The scope of the series is broad, including titles in machine learning, pattern recognition, predictive analytics, business analytics, visualization, programming, software, learning analytics, data collection and wrangling, interactive graphics, reproducible research, and more. The inclusion of examples, applications, and code implementation is essential.

Please **Contact Us** if you have an idea for a book for the series.

Forthcoming

By **Sutaip L.C. Saw**

December 03, 2024

Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is...

Forthcoming

By **Benoit Liquet, Sarat Moka, Yoni Nazarathy**

October 03, 2024

Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms, and progresses through the key ideas of deep learning. These ideas ...

Forthcoming

By **Douglas Gray, Evan Shellshear**

September 10, 2024

The field of artificial intelligence, data science and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven. ...

Forthcoming

By **Antony Unwin**

August 28, 2024

Data graphics are used extensively to present information. Understanding graphics is a lot about understanding the data represented by the graphics, having a feel not just for the numbers themselves, the reliability and uncertainty associated with them, but also for what they mean. This book ...

Forthcoming

By **Tiffany Timbers, Trevor Campbell, Melissa Lee, Joel Ostblom, Lindsey Heagy**

August 22, 2024

Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes ...

Forthcoming

By **Rafael A. Irizarry**

August 01, 2024

Unlike the first edition, the new edition has been split into two books. Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis ...

By **Alex Gold**

June 19, 2024

Data Scientists are experts at analyzing, modelling and visualizing data but, at one point or another, have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter. Born out of the agile software movement, DevOps is a set of practices, ...

By **Martin Hugh Monkman**

May 07, 2024

The Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With that context, readers will work through practical solutions to resolving problems in data using the ...

By **Matthias Bannert**

April 17, 2024

Research Software Engineering: A Guide to the Open Source Ecosystem strives to give a big-picture overview and an understanding of the opportunities of programming as an approach to analytics and statistics. The book argues that a solid "programming" skill level is not only well within reach for ...

By **Clive Beggs**

March 11, 2024

Sports analytics is on the rise, with top soccer clubs, bookmakers, and broadcasters all employing statisticians and data scientists to gain an edge over their competitors. Many popular books have been written exploring the mathematics of soccer. However, few supply details on how soccer data can ...

By **Bradley J. Congelio**

December 19, 2023

It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans, and commentators, throughout broadcasts, regularly use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, ...

By **Paula Moraga**

December 08, 2023

Spatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques...