Capstone Brief: Domain Capstone (UNPUBLISHED)

# DS Project Brief 3 — Domain Capstone (UNPUBLISHED DRAFT) > Status: UNPUBLISHED draft. Do not enroll learners until the P5 capstone > milestone placeholder (`data-scientist` track, `is_required=False`) is > flipped to required. Ref: DS track seed > `backend/scripts/seed_career_track_data_scientist.py`. ## 1. Objective An end-to-end, interview-defendable capstone in ONE chosen domain (fintech risk, e-commerce growth, or SaaS retention): frame the business problem, analyze messy data, ship at least one predictive or causal artifact, and present the recommendation as if to a hiring manager. ## 2. Deliverables 1. Scoping memo (≤1 page): decision to inform, success metric, constraints. 2. EDA notebook: data quality audit + ≥ 3 decision-relevant findings. 3. Model or causal artifact: trained/evaluated model OR experiment analysis with power/sample-size reasoning. 4. Stakeholder memo (≤2 pages): recommendation, risks, what you would do next. 5. 10-minute recorded walkthrough defending all of the above. ## 3. Constraints - One domain only; depth beats breadth. - All claims traceable to a table/figure/model output in the submission. - At least one "what would change my recommendation" sensitivity check. ## 4. 100-Point Rubric | # | Criterion | Points | |---|-----------|--------| | C1 | Scoping: decision, metric, and constraints crisply defined | 10 | | C2 | Data quality audit (missingness, joins, outliers, definitions) | 15 | | C3 | EDA: ≥ 3 findings that actually move the recommendation | 15 | | C4 | Model/causal artifact: correct methodology, honest evaluation | 20 | | C5 | Sensitivity check: one credible challenge to the recommendation | 10 | | C6 | Stakeholder memo: clear recommendation + risks + next steps | 15 | | C7 | Walkthrough: defends choices, answers "why not X" credibly | 10 | | C8 | Reproducibility (pinned env, re-runnable notebook/scripts) | 5 | | | **Total** | **100** | Pass: ≥ 80 overall AND C4 ≥ 14/20 (methodology is a hard gate).