Course Overview - Spring 2025

Description

This course will focus on developing the practical skills needed to be an effective data scientist. In particular, students will learn how to collect and clean data, and make effective visualizations with industry-standard tools. Through realistic case-studies, students will gain hands-on experience working with various types of data including: tabular, time-series, geo-spatial and network data. A strong emphasis will be placed on clear communication, interpretability and social responsibility.

Course Slack: You can find the course slack channel here

Instructor

Prof. Gabe Hope (he/him)

Email: ghope@g.hmc.edu

Office: MacGregor 311

Office hours (MCG 311):

• Mondays 3-4pm

• Thursdays 3:30-5pm

About me

• What to call me: You can call me any combination of Prof./Professor/Dr. and Hope/Gabe. My full name is actually John Gabriel Hope.
• Where I’m from: I grew up in New York City, but I’ve been living in southern California for the past 7 years.
• Where I went to school: I earned my Ph.D. from the University of California, Irvine advised by Erik Sudderth. Before that I attended Brown University at the start of my Ph.D. and did my undergraduate studies at Washington University in St. Louis.
• My research interests: My research focuses on using neural networks to find interpretable structures in data. I mainly focus on image data, though I have also worked on analyzing motion-capture, audio and climate data among other things!

Grutor

Topics covered

• Frameworks for data visualization

• Data wrangling and preprocessing with Pandas

• Effective and accessibile visualization design

• Perception of visualization

• Timeseries, geo-spatial and network data analysis

• Web technologies for visualization including Javascript and D3.js

• Interaction and animation techniques for visualization.

Syllabus and Class Policies

Please see the navigation bar above for information on the class schedule, course policies, grading, reference materials, software and more.