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CS 65 W — Data Analysis with Python

Quarter: Fall
Course Format: Online (System Requirements)
Duration: 6 weeks
Date(s): Oct 14—Nov 22
Drop Deadline: Oct 17
Unit: 1
Tuition: $420
Instructor(s): Matt Harrison
Limit: 45
Status: Closed
Please Note: Online courses have a new refund policy. The full tuition refund deadline for this course is October 17th at 5:00 pm (PT); 50% tuition refund deadline is October 22nd at 5:00 pm (PT).
Oct 14—Nov 22
6 weeks
Drop By
Oct 17
1 Unit
Matt Harrison
Please Note: Online courses have a new refund policy. The full tuition refund deadline for this course is October 17th at 5:00 pm (PT); 50% tuition refund deadline is October 22nd at 5:00 pm (PT).
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 data analysis with 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 are popular tools that are both open source and very popular among data scientists and analysts in both academia and industry. These tools include Jupyter Notebooks, Pandas, plotting with matplotlib and seaborn, and some basics of machine learning using scikit-learn.

Data is often messy, so we will learn how to clean it up. 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.


  • Course sizes are limited.
    You won't have 5,000 classmates. This course's enrollment is capped at 45 participants.

  • Frequent interaction with the instructor.
    You aren't expected to work through the material alone. Instructors will answer questions and interact with students on the discussion board and through weekly video meetings.

  • Study with a vibrant peer group.
    Stanford Continuing Studies courses attract thoughtful and engaged students who take courses for the love of learning. Students in each course will exchange ideas with one another through easy-to-use message boards as well as optional weekly real-time video conferences.

  • Direct feedback from the instructor.
    Instructors will review and offer feedback on assignment submissions. Students are not required to turn in assignments, but for those who do, their work is graded by the instructor.

  • Courses offer the flexibility to participate on your own schedule.
    Course work is completed on a weekly basis when you have the time. You can log in and participate in the class whenever it's convenient for you. If you can’t attend the weekly video meetings, the sessions are always recorded for you and your instructor is just an email away.

  • This course is offered through Stanford Continuing Studies.
    To learn more about the program, visit our About Us page. For more information on the online format, please visit the FAQ page.

Previous programming experience is useful, but not essential. The instructor’s book, Illustrated Guide to Python 3, will equip students with the basics of Python.

Matt Harrison, Principal Consultant & 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:

(Recommended) Matt Harrison, Illustrated Guide to Python 3 (ISBN 978-1977921758)
(Recommended) Matt Harrison, Machine Learning Pocket Reference (ISBN 978-1492047544)