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.
Deliverables
Submitted in order; each gates the next:
- Proposal — 1–2 page PDF: problem, dataset(s), method, privacy considerations, milestone plan. Must be approved before implementation.
- 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
| Deliverable | Format | Notes |
|---|---|---|
| Proposal | Title, names, references. | |
| Code repo | Git URL | README + pinned env + seed; no secrets/PII. |
| Final report | Captioned figures; cite every dataset and claim. | |
| Notebook | .ipynb (v4) | intro → data-load → analysis → privacy → conclusion. |
Rubric
| Component | Weight | What we look for |
|---|---|---|
| Proposal | 10% | Clear question, feasible scope, privacy awareness. |
| Implementation | 40% | Correctness, reproducibility, code quality. |
| Privacy analysis | 20% | Explicit threat model + an evaluated mitigation. |
| Final report | 20% | Honest claims, uncertainty, limitations. |
| Presentation | 10% | Clear communication of problem and results. |
Timeline
Dates follow the course schedule (Tue/Thu, 1:00 – 2:15 PM).
| Milestone | Due |
|---|---|
| Proposal | Week 4 |
| Progress check-in (repo + partial results) | Week 11 |
| Final report, notebook & presentation | Week 15 |
Examples & 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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