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CS 06 — An Introduction to Data Visualization with Python

Quarter: Fall
Day(s): Wednesdays
Course Format: Live Online (About Formats)
Duration: 8 weeks
Date(s): Oct 6—Dec 1
Time: 7:00—8:50 pm (PT)
Refund Deadline: Oct 8
Unit: 1
Tuition: $445
Instructor(s): Charlie Flanagan
Limit: 60
Class Recording Available: Yes
Status: Open
Please Note: No class on November 24
Fall
Live Online(About Formats)
Wednesdays
7:00—8:50 pm (PT)
Date(s)
Oct 6—Dec 1
8 weeks
Refund Date
Oct 8
1 Unit
Fees
$445
Instructor(s):
Charlie Flanagan
Limit
60
Recording
Yes
Open
Please Note: No class on November 24
In today’s world, being able to tell a story with data is essential. This course will teach students how to create and publish stunning data visualizations and interactive graphics using the latest open source tools. In addition to lectures and labs, guest speakers who are data visualization experts from leading tech companies will show how they leverage the tools we will use in class. We will begin with an introduction to loading and pre-processing data using Python and SQL. We will survey the ecosystem of visualization tools, explaining the pros and cons of each, reviewing the different visualization chart types, and discussing what each is best at conveying. We will then move to creating visualizations using popular open source libraries such as Matplotlib and Seaborn. We will also try out Altair, an open source package that will allow us to build interactive and connected visualizations. The course will consist of lectures, live coding demonstrations, hands-on practice, and guest speakers. We will hold lab sections each week after class so students can apply their learning to their own projects, with help from the instructor. Students will finish the course with a portfolio of visualizations that they understand deeply and can leverage going forward.

Some previous basic coding experience will be helpful but is not required as we will be editing existing code.

CHARLIE FLANAGAN
Data Scientist, Google

Charlie Flanagan spent ten years as a quantitative analyst in the financial industry before coming to Google. He received an MS in software engineering from Harvard and an MBA from Columbia, and is a Chartered Financial Analyst.