FAST-05 Projects: Applied Data Science and Machine Learning for Business Users
Provider: 7formations | Course: FAST-05 | Instructor: Wadood Chaudhary | One capstone and two mini projects
Projects apply the course to models and forecasts trainees meet at work, without building any. Scoring uses Not yet / Meets / Strong. Completion requires Meets on every capstone criterion; mini projects are formative unless the employer asks for scoring. One resubmission allowed. Use fictional, public, or approved aggregated data only; never customer personal information or PHI.
Capstone: Decision Memo on a Model or Vendor AI Feature
Brief. Choose a model, score, forecast, or vendor AI feature used (or proposed) in your role: CRM lead scoring, ERP demand forecast, claim or fraud triage, quality flags, scheduling predictions. If you have none, use ClaimGuard (Lab 5) or the clinic no-show model (Lab 3). Write a 2-page decision memo for a non-expert manager evaluating whether and how to use it, and present it in three minutes.
Deliverables
- Prediction map (W1 format).
- Metrics in business terms: what errors cost in each direction, and the threshold or override approach you recommend.
- Data and fairness review: 3+ data questions or problems, who could be harmed.
- One verified analysis: an AI-assisted finding confirmed by manual calculation (fictional or approved data).
- Vendor or owner questions (6+) and a monitoring plan (owner, measure, trigger, action).
- 100-word explain-it-back for an affected customer or coworker.
- Recommendation: adopt / adopt with conditions / pilot / don't adopt, with reasons.
Step plan
| Step | When | What you do |
|---|---|---|
| 1 | After session 1 | Choose the model; W1 map |
| 2 | After session 2 | Costs and threshold reasoning |
| 3 | After session 3 | Data and fairness review |
| 4 | After session 4 | Verified analysis |
| 5 | Session 5 | Questions, monitoring, explain-it-back; present |
Rubric
| Criterion | Not yet | Meets | Strong |
|---|---|---|---|
| Metric interpretation | Accuracy only or no costs | Metrics tied to business cost both directions | Threshold or override grounded in cost and capacity |
| Data judgment | Fewer than 3 issues | 3+ real data or fairness issues, specific | Issues sized; affected groups and mitigations named |
| Verification | No manual check | One AI finding confirmed manually | Differences explained and reconciled |
| Communication | Jargon; recommendation unclear | Non-expert understands decision and reasons | Explain-it-back scored clear by peers; next steps owned |
| Governance | No monitoring | Owner, measure, trigger, action | Linked to relevant rules (e.g., MA DOI Bulletin 2024-10, employer policy) |
Strong vs weak example (recommendation paragraph)
| Weak | Strong |
|---|---|
| "ClaimGuard is 95% accurate, so we should use it. AI is the future and our competitors use it." | "Pilot ClaimGuard for 90 days on claims from policies older than 2 years only, at a threshold that keeps flags within adjuster capacity (about 80/month). The vendor's 95% accuracy is on Midwest auto claims and doesn't tell us how many frauds it would catch for us; we've asked for homeowners precision and recall and for ZIP-code proxy testing. Claims supervisor reviews flagged-vs-confirmed monthly; if fewer than 15% of flags are confirmed for two months, we pause." |
Mini project 1: Metric Translator
Brief. Pick three metrics from a dashboard, vendor sheet, or report you see (or use the Lab 2 table). Translate each into a plain sentence with business consequences for your team.
Deliverables. Three translations; for each: the metric, its value, a plain-language sentence, what a wrong decision costs, one question to ask the owner.
Step plan. (1) Pick metrics; (2) define each in one sentence; (3) attach costs; (4) test the sentences on a coworker; (5) revise.
Rubric
| Criterion | Not yet | Meets | Strong |
|---|---|---|---|
| Accuracy | Definitions wrong | Definitions correct | Includes base rate context |
| Plain language | Jargon remains | A coworker understood all three | Uses real counts ("14 of 20") |
| Business link | No consequence | Consequence and question per metric | Suggests a better metric where needed |
Strong vs weak example. Weak: "Precision is 28%, which is the positive predictive value." Strong: "Of every 50 claims the model flags, about 14 are fraud. So adjusters spend most review time on legitimate claims; ask whether a 0.6 threshold catches more fraud within our capacity."
Mini project 2: Forecast Review Routine
Brief. Design a monthly forecast-vs-actuals review for a forecast your team uses (or Bayside's BP-2207). Include error tracking, out-of-range alerts, override logging, and override review.
Deliverables. A one-page routine; a template sheet (columns: month, forecast, low, high, actual, abs error, outside range?, override, reason, approver, override error); three months of fictional or approved example entries; a rule for when to request model retraining.
Step plan. (1) Pick the forecast; (2) build the template; (3) fill examples; (4) verify one AI-computed error by hand; (5) write the retraining and escalation rule.
Rubric
| Criterion | Not yet | Meets | Strong |
|---|---|---|---|
| Error tracking | No error measure | Abs error and MAE by period | Separates periods before/after known changes |
| Override discipline | Overrides unlogged | Reason, source, approver, review | Override accuracy compared to original forecast |
| Verification | AI numbers unchecked | One checked manually | Template formulas documented |
Strong vs weak example. Weak: "Check the forecast monthly." Strong: "First Monday each month the planner logs actuals; any month outside the 80% range for two months triggers a review with sales; overrides are logged with source and compared to the original forecast after actuals arrive; if override error is lower three months running, request retraining."