Term Project
Design and implement a substantial big-data or privacy artifact, end to end
Project Manual
Overview
The term project for CSCI 49395 — Big Data Analytics and Applied Privacy (Fall 2026) requires you to design and implement a substantial, end-to-end big-data or privacy artifact and to report on it honestly. Your project must engage a real privacy or scale challenge (e.g. anonymization, differential privacy, a scalable pipeline, or privacy risk in ML) rather than simply calling a library. Separate what you measured from what you believe.
Course Project Slides
Slides from the project module.
Project Reference
The reference document below summarizes scope, deliverables, and grading.
Deliverables Suggestions
Suggested deliverables; final requirements will be announced by the professor.
- Proposal — 1–2 page PDF: problem, dataset(s), method, privacy considerations, milestone plan.
- Code repository — Git repo with a
README, a pinned environment, a fixed seed, and one-command reproduction. Raw data read-only; no committed secrets or PII. - Final report (PDF) — 6–10 pages: intro, related work, method, results with effect sizes / uncertainty, a dedicated privacy analysis, limitations, conclusion.
- Jupyter notebook — runs top-to-bottom (Restart & Run All): intro → data-load → analysis → privacy analysis → conclusion.
Required Format
To be announced.
Rubric
Timeline
Dates follow the course schedule (Tue/Thu, 1:00 – 2:15 PM).
| Milestone | Due |
|---|---|
| Team information and initial discussion | Week 3 |
| Proposal and presentation | Week 6 |
| Progress check-in (repo + partial results) | Week 11 |
| Final report, notebook & presentation | Week 15 |
Examples & Templates
The professor will provide updated templates.
Get feedback from the instructor early and often. Never commit PII, credentials, or licensed data. Report the honest result, including nulls — proposals that need revision must be resubmitted and approved before you begin.
Submissions
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