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» Home » Data Science: A First Introduction (Python Version)

Data Science: A First Introduction (Python Version)

Category: CC BY-NC-SA, Mathematics and Statistics, Textbook, UBC Vancouver, Undergraduate Lower Division

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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

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Except where otherwise noted, this website is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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