Data Science - A First Introduction

Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.

List of contents

1. R and the tidyverse, 2. Reading in data locally and from the web, 3. Cleaning and wrangling data, 4. Effective data visualization, 5. Classification I: training & predicting, 6. Classification II: evaluation & tuning, 7. Regression I: K-nearest neighbors, 8. Regression II: linear regression, 9. Clustering, 10. Statistical inference, 11. Combining code and text with Jupyter, 12. Collaboration with version control, 13. Setting up your computer

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

Summary

Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.

Product details

• Authors: Trevor Campbell, Campbell Trevor, Melissa Lee, Lee Melissa, Tiffany Timbers, Tiffany (University of British Columbia) Timbers, Tiffany-Anne Timbers, Tiffany-Anne (University of British Colum Timbers
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 30.04.2022
• EAN: 9780367532178
• ISBN: 978-0-367-53217-8
• No. of pages: 456
• Series: Chapman & Hall/CRC Data Science Series
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Social sciences, law, business > Business > Miscellaneous; Artificial Intelligence, BUSINESS & ECONOMICS / Statistics, MATHEMATICS / Probability & Statistics / General, COMPUTERS / Machine Theory, environmental science, engineering & technology, Probability & statistics, Automatic control engineering, Databases, Probability and statistics, Environmental science, engineering and technology, COMPUTERS / Data Science / Data Analytics