Assignments¶
Two graded, individual assignments. Each assignment folder contains student
instructions (README.md), any provided data/starter material, a Streamlit
tutorial (Assignment 2), and an instructor-solution.md with a complete
worked solution, expected outputs, and rubric application notes.
| Assignment | Released | Due | CLO | Focus |
|---|---|---|---|---|
assignment-01-data-cleaning-eda |
W6 S12 | W8 S15 | CLO-1 | Clean a messy dataset; documented decisions; structured EDA and findings |
assignment-02-api-streamlit-app |
W9 S17 | W15 S30 | CLO-1, CLO-3 | API/data acquisition → clean → EDA → interactive Streamlit app, on GitHub |
Conventions¶
- Each brief states: learning objectives, problem statement, requirements, dataset requirements, tasks, expected deliverables, GitHub requirements, README requirements, grading rubric, CLO mapping, and academic integrity rules.
- Notebooks must be executed and saved with visible outputs; code must run
from a clean environment (
pip install -r ../requirements.txt). - Submissions are private GitHub repos with a visible commit history (≥ 4 commits for Assignment 1, ≥ 6 for Assignment 2).
- Students disclose AI assistance per the AI-use policy in
../assessment-plan.md.
Machine-learning modeling is assessed through Labs 17–22, Quiz 2, the final exam, and the final project's modeling component — not through a separate assignment.