Author(s): Tiffany Timbers, Trevor Campbell, Melissa Lee, Joel Ostblom, and Lindsey Heagy
Description:
This textbook provides an approachable introduction to the world of data science. In this book, you will learn how to identify common problems in data science and solve them with reproducible and auditable workflows using the Python programming language. You will spend the first four chapters learning how to load, clean, wrangle, and visualize data. In the next six chapters, you will learn about common predictive and inferential methods, including classification, regression, clustering, and estimation. In the final chapters, you will learn about Jupyter notebooks, version control, and the computer setup needed to follow along with the book.
Item Type:
Textbook
Subject Area:
Mathematics and Statistics
Faculty/Department:
Faculty of Science
License Type:
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Audience:
Undergraduate Lower Division
Technical Format:
- Textbook
Language:
English
Development Location:
UBCV
Ancillary Resources:
Accompanying Jupyter notebook exercises are available at https://worksheets.datasciencebook.ca
Related Resources:
There is also a version of the textbook for the R programming language available at https://datasciencebook.ca
Courses:
DSCI 100
Funding Affiliation:
UBCO OER Fund
Author Supplied-Keywords:
data science, Python, programming, Jupyter, visualization, data cleaning, classification, regression, clustering, inference
