The Essentials of Data Science: Knowledge Discovery Using R

Name: The Essentials of Data Science: Knowledge Discovery Using R
Brand: Taylor & Francis
SKU: 9781498740005
Price: 376.0 NZD
Availability: InStock

By Graham J. Williams

Series: Chapman & Hall/CRC The R Series

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Summary

The Essentials of Data Science: Knowledge Discovery Using R by Graham J. Williams offers a practical introduction to data science using free and open source software. The book takes readers through data analysis and machine learning with a programming-by-example approach, helping develop data skills through reusable frameworks and real-world case studies. It begins with an overview of data science, followed by step-by-step R programming for data analysis, visualisation, and predictive modelling, emphasising literate programming and a consistent style throughout.
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Format: Hardback

$376⁰⁰

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The Essentials of Data Science: Knowledge Discovery Using R

$376⁰⁰

Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

Ideal for students, data science practitioners, and professionals interested in learning R programming within the context of data analysis and machine learning.

This book presents data science material useful to data scientists. As a practitioner, the author brings a practical view, with a hands-on presentation useful to other practitioners. He concentrates on the current generation of R packages, including Hadley Wickam's suite of packages, such as tidyr, dplyr, lubridate, stringr, and ggplot2.

Description

The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically guides an accessible journey through data analysis and machine learning to discover and share knowledge from data.

Building on over thirty years of experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners align with the practice of data science while building their data skills. Proven frameworks are provided as reusable templates. Real-world case studies offer insight for the data scientist to swiftly adapt the templates to new tasks and datasets.

The book begins by introducing data science and reviews R’s capabilities for analysing data by writing computer programs, which are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are emphasised throughout the book.

Book Hero Magic summarised reviews for this book. While it's new and still learning, it may not be perfect - your feedback is welcome! HOW HAS THIS BEEN REVIEWED?

Praised for blending key data science topics with R programming, the book is recommended for both college students starting careers in data science and professionals seeking to deepen their knowledge or pivot careers. Reviewers highlight its suitability as a course textbook or supplementary resource, offering a self-contained introduction to R and RStudio alongside hands-on learning through the rattle package.

Book Details

INFORMATION

ISBN: 9781498740005

Publisher: Taylor & Francis Inc

Format: Hardback

Date Published: 13 July 2017

Country: United States

Imprint: Chapman & Hall/CRC

Audience: General / adult, Tertiary education, Professional and scholarly

DIMENSIONS

Width: 156.0mm

Height: 234.0mm

Weight: 700g

Pages: 344

Collections

Computing & Technology Education & Reference Graham J. Williams Non-Fiction Science & Nature Series: Chapman & Hall/CRC The R Series

About the Author

Graham J. Williams is Director of Data Science with Microsoft and Honorary Associate Professor with the Australian National University. He is also Adjunct Professor with the University of Canberra. He was previously Senior Director of Analytics with the Australian Taxation Office, Lead Data Scientist with the Australian Government's Centre of Excellence in Data Analytics, and International Visiting Professor of the Chinese Academy of Sciences. Over three decades , Graham has been an active machine learning researcher and author of many publications and software including Rattle. As a practitioner of data science he has deployed solutions in areas including finance, banking, insurance, health, education and government. He is also chair and steering committee member of international conferences in knowledge discovery, artificial intelligence, machine learning, and data mining.