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CS 65 W — Data Analysis with Python

Quarter: Fall
Course Format: Flex Online (About Formats)
Duration: 6 weeks
Date(s): Oct 12—Nov 20
Drop Deadline: Oct 15
Unit: 1
Tuition: $420
Instructor(s): Matt Harrison
Limit: 60
Status: Closed
Please Note: Some of our refund deadlines have changed. See this course's drop deadline above and click here for the full policy.
Fall
Flex Online(About Formats)
Date(s)
Oct 12—Nov 20
6 weeks
Drop By
Oct 15
1 Unit
Fees
$420
Instructor(s):
Matt Harrison
Limit
60
Closed
Please Note: Some of our refund deadlines have changed. See this course's drop deadline above and click here for the full policy.
We live in a world surrounded by data. But how do we observe and extract value from this data? How do we explore and begin to understand and visualize large data sets? Are there relationships in the data or unusual observations we can uncover? This course will help students learn how to perform data analysis using Python. We will acquire data, examine it, clean it up, visualize it, and begin to infer conclusions from it. We will walk through the basics of data analysis using the Python toolchain. These popular tools are both open source and very popular among data scientists and analysts in both academia and industry. They include the Jupyter Notebook, Pandas, plotting with Matplotlib and Seaborn, and some basics of machine learning using Scikit-Learn. We will explore categorical data that provides labels such as the make of a car or the web browser of a visitor to a website. We will also discuss charts, tables, and correlations, and explore some color theory. Finally, we will dive into numerical data, including how to create, interpret, and plot data with multiple dimensions. Students will leave the course able to analyze a data set from start to finish, providing graphical and numerical summaries, correlations, and outliers.

Previous programming experience is useful, but not essential.

Matt Harrison, Principal Consultant and Corporate Trainer, MetaSnake

Matt Harrison has been using Python since 2000. He runs a Python, data science, and corporate training consultancy. He is the author or a co-author of several books on Python. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage.

Textbooks for this course:

(Required) Harrison, Illustrated Guide to Python 3 (ISBN 978-1977921758)
(Required) Harrison, Machine Learning Pocket Reference (ISBN 9781492047537)
DOWNLOAD THE PRELIMINARY SYLLABUS » (subject to change)