Chapman & Hall/CRC Data Science Series

Reflecting the interdisciplinary nature of the field, this new data science 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, artificial intelligence, 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.

  • Cybersecurity Analytics book cover

    Cybersecurity Analytics

    1st Edition

    By Rakesh M. Verma, David J. Marchette

    Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware…

    Hardback – 2019-11-15 
    Chapman and Hall/CRC
    Chapman & Hall/CRC Data Science Series

  • Introduction to Data Science: Data Analysis and Prediction Algorithms with R book cover

    Introduction to Data Science

    Data Analysis and Prediction Algorithms with R, 1st Edition

    By Rafael A. Irizarry

    Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop…

    Hardback – 2019-10-22 
    Chapman and Hall/CRC
    Chapman & Hall/CRC Data Science Series

  • Feature Engineering and Selection: A Practical Approach for Predictive Models book cover

    Feature Engineering and Selection

    A Practical Approach for Predictive Models, 1st Edition

    By Max Kuhn, Kjell Johnson

    The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset…

    Hardback – 2019-08-02
    Chapman and Hall/CRC
    Chapman & Hall/CRC Data Science Series

  • Probability and Statistics for Data Science: Math + R + Data book cover

    Probability and Statistics for Data Science

    Math + R + Data, 1st Edition

    By Norman Matloff

    Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes…

    Paperback – 2019-06-20
    Chapman and Hall/CRC
    Chapman & Hall/CRC Data Science Series