akube@uchicago.edu
Data Science Institute
The University of Chicago
Teaching
My teaching focuses on helping students build confidence with data, code, statistical reasoning, and ethical decision-making. I design courses and activities that emphasize active learning, authentic data problems, and clear communication about technical ideas.
This page collects selected teaching materials, classroom activities, student resources, and course information from my work in data science education.
Featured Teaching Resources
Classroom Activities
Short activities, discussion prompts, and applied exercises for topics such as machine learning evaluation, data ethics, debugging, recursive thinking, and communicating with data.
Student FAQs and Guides
Resources I use to help students approach common challenges: debugging code, preparing for programming exams, working on open-ended projects, and asking effective technical questions.
Course Materials
Selected syllabi, assignments, and public-facing materials from courses in introductory data science, computer science for data science, machine learning, and data science ethics.
Teaching Projects
Curriculum and pedagogy projects, including work on transferable data science curriculum, interactive Python labs, and teaching data science ethics through real-world case studies.
Courses
I regularly teach across the undergraduate data science curriculum, including:
- DATA 119: Introduction to Data Science II
- DATA 120: Computer Science for Data Science
- DATA 221: Introduction to Machine Learning
- DATA 259: Ethics, Fairness, Responsibility, and Privacy in Data Science
A fuller list of courses and syllabi is available in my CV.
Teaching Philosophy
My teaching is grounded in the idea that students learn data science best by doing data science: asking questions, making mistakes, debugging, explaining their reasoning, and connecting technical choices to human consequences.