Data Science: A First Introduction (Chapman & Hall/CRC Data Science Series) - Softcover

Timbers, Tiffany; Campbell, Trevor; Lee, Melissa

Softcover

ISBN 10: 0367524686 ISBN 13: 9780367524685

Publisher: Chapman and Hall/CRC, 2022

Synopsis

Data Science: A First Introduction focuses on using the R 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.

The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows.

Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects.

The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.

About this title

About the Author

Tiffany Timbers is an Assistant Professor of Teaching in the Department of Statistics and Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia.

Trevor Campbell is an Assistant Professor in the Department of Statistics at the University of British Columbia.

Melissa Lee is an Assistant Professor of Teaching in the Department of Statistics at the University of British Columbia

From the Back Cover

Data Science: A First Introduction focuses on using the R 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. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia's DSCI100: Introduction to Data Science course.

Edition details

• Publisher: Chapman and Hall/CRC
• Publication date: 2022
• Language: English
• ISBN 10: 0367524686
• ISBN 13: 9780367524685
• Binding: Paperback
• Edition number: 1
• Number of pages: 456