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CS 68 — Machine Learning for Business with Python

Quarter: Spring
Day(s): Wednesdays
Course Format: Live Online (About Formats)
Duration: 8 weeks
Date(s): Apr 7—May 26
Time: 7:00—8:50 pm (PT)
Refund Deadline: Apr 9
Unit: 1
Tuition: $445
Instructor(s): Charlie Flanagan
Status: Open
DOWNLOAD THE SYLLABUS » (subject to change)
Live Online(About Formats)
7:00—8:50 pm (PT)
Apr 7—May 26
8 weeks
Refund Date
Apr 9
1 Unit
Charlie Flanagan
DOWNLOAD THE SYLLABUS » (subject to change)
Data is the hidden asset that all companies have, though few are using it to its full potential. Yet in recent years, tools and techniques to unlock the power of data have become extremely accessible to all. In this course, we will explore the amazing array of powerful business insights that analysts can unlock through the application of relatively easy-to-use, open source tools such as Python, Scikit-Learn, TensorFlow, CausalImpact, and Altair. Each week, we will look at a different real-life business-related problem and use the latest data science tools and techniques to solve it. For example, we will measure the causal impact of a marketing campaign that a business has undertaken, predict whether a customer is likely to leave, or determine how much to charge for a new product. This will allow students to intuitively understand the entire workflow, from identifying a business problem to gathering the data and implementing the solution in code in a scalable and repeatable way. We will also have guest speakers from data science teams in the tech industry who are tackling these types of problems daily, plus activities such as optional virtual coffee meetups and conferences. Students will come away from the course understanding of how to formulate business problems for a data science setting and the tools for solving those problems.

Some prior experience with Python will be helpful but is not required. Some pre-class virtual office hours will be available for students who need extra help.

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.