Brief
One focused decision with a supplied neighborhood; approximately 3–5 prepared-learner hours.
THE ANALYST / INSTRUCTOR PLANNING DESK
RETURN TO LEARNER WORKBENCHTHE ANALYST / NINE-ASSIGNMENT SEQUENCE
Plan a progression through the work.
This desk helps instructors sequence nine workplace assignments across SQL, Python, data judgment, and professional handoff. It contains no sample conclusions. Prepared-hour estimates assume a learner can work independently; newcomer ranges assume active instructor support.
WORKLOAD BANDSNOT GRADES
Investigation
Several assets and competing explanations; approximately 6–12 hours.
Decision
Multi-stage evidence and several professional artifacts; approximately 10–18 hours.
Practicum
End-to-end build, audit, forecasting, or deployment work; approximately 16–30+ newcomer hours.
RECOMMENDED SEQUENCE24-WEEK PROGRAM / 16-WEEK SEMESTER
| # | Assignment | Band | Prepared | Newcomer | SQL | Python | State |
|---|---|---|---|---|---|---|---|
| 01 | The Monday ScorecardCustomer Insights Analyst | Brief | 3–4h | 5–8h | 2/5 | 2/5 | connected |
| 02 | The Quarter That MovedCommercial Data Transition Analyst | Investigation | 5–8h | 8–12h | 3/5 | 3/5 | connected |
| 03 | The Navigation VoteProduct Experimentation Analyst | Decision | 8–12h | 12–18h | 3/5 | 4/5 | connected |
| 04 | Rollback Before DawnConnected Reliability Analyst | Decision | 6–9h | 10–16h | 4/5 | 4/5 | connected |
| 05 | The 7:30 Capacity CallService Capacity Analyst | Decision | 10–16h | 18–26h | 4/5 | 5/5 | connected |
| 06 | Forty-Eight Hours of StockSupply Planning Data Scientist | Practicum | 12–18h | 20–30h | 4/5 | 5/5 | connected |
| 07 | The Orion RenewalSenior Operations Analyst | Decision | 10–16h | 18–28h | 5/5 | 4/5 | connected |
| 08 | The Queue Nobody OwnsApplied ML Analyst | Practicum | 12–18h | 20–30h | 4/5 | 5/5 | connected |
| 09 | Too Good to ShipModel Risk Analyst | Practicum | 8–14h | 16–24h | 3/5 | 5/5 | connected |
The Monday Scorecard
Reconcile two conflicting satisfaction figures before the executive review.
- SQL CORE
- Profile grains, scales, cohorts, duplicate interactions, and response coverage.
- PYTHON CORE
- Reproduce the profile in Pandas, visualize scale/cohort differences, and create the scorecard artifact.
- PREREQUISITES
- Tables and data types · Basic SELECT/GROUP BY · Pandas introduction
- HANDOFF
- 4 professional artifacts · core-analysis runtime
SQL2
Python2
Data complexity2
Statistical reasoning1
Decision ambiguity3
Deliverable load2
The Quarter That Moved
Certify Q2 orders, revenue, and fulfillment timing after an acquisition cutover.
- SQL CORE
- Construct a stable order fact across headers, lines, events, and shipments.
- PYTHON CORE
- Profile collisions and clock errors, build reusable exception flags, test reconciliations, and certify an extract.
- PREREQUISITES
- Python functions · Pandas joins · SQL joins and aggregation
- HANDOFF
- 5 professional artifacts · core-analysis runtime
SQL3
Python3
Data complexity3
Statistical reasoning2
Decision ambiguity3
Deliverable load3
The Navigation Vote
Determine whether a mobile-navigation experiment warrants rollout.
- SQL CORE
- Build one row per assignment and reconcile exposure, sessions, events, and orders.
- PYTHON CORE
- Run balance checks, estimate effects and uncertainty, inspect distributions, and separate confirmatory from exploratory slices.
- PREREQUISITES
- Probability and sampling · Confidence intervals · SQL cohort construction
- HANDOFF
- 5 professional artifacts · statistics runtime
SQL3
Python4
Data complexity3
Statistical reasoning4
Decision ambiguity4
Deliverable load4
Rollback Before Dawn
Recommend global rollback, scoped containment, or monitored continuation during a storm.
- SQL CORE
- Build point-in-time asset, telemetry, alert, weather, and field-operation evidence lanes.
- PYTHON CORE
- Diagnose missingness, compare regions and periods, quantify counterevidence, and visualize decision thresholds.
- PREREQUISITES
- Missing-data mechanisms · Time-aware joins · Exploratory visualization
- HANDOFF
- 5 professional artifacts · statistics runtime
SQL4
Python4
Data complexity5
Statistical reasoning4
Decision ambiguity5
Deliverable load4
The 7:30 Capacity Call
Build a morning risk view for appointments likely to miss their service window.
- SQL CORE
- Create a point-in-time appointment feature mart at a stable entity and scoring time.
- PYTHON CORE
- Build sklearn pipelines, compare a baseline, use forward validation, inspect calibration/errors, and design an intervention threshold.
- PREREQUISITES
- Supervised learning · Feature pipelines · Temporal validation · Classification metrics
- HANDOFF
- 7 professional artifacts · modeling runtime
SQL4
Python5
Data complexity4
Statistical reasoning4
Decision ambiguity4
Deliverable load5
Forty-Eight Hours of Stock
Forecast 21-day SKU/warehouse risk and recommend transfers, expedites, or substitutions.
- SQL CORE
- Reconcile movement, demand, receipt, transfer, and vendor facts into a complete daily spine.
- PYTHON CORE
- Backtest honest baselines, forecast intermittent demand, simulate lead-time risk, and produce a constrained action file.
- PREREQUISITES
- Time-series validation · Forecast error · Simulation · Operational constraints
- HANDOFF
- 8 professional artifacts · statistics runtime
SQL4
Python5
Data complexity5
Statistical reasoning5
Decision ambiguity5
Deliverable load5
The Orion Renewal
Audit a claimed 12% optimizer gain and make a procurement recommendation.
- SQL CORE
- Define stable route, stop, work-order, visit, and workforce outcomes without fanout.
- PYTHON CORE
- Plot trends and heterogeneity, run sensitivity/placebo analyses, and build the board-ready evidence package.
- PREREQUISITES
- Metric design · Join reconciliation · Quasi-experimental reasoning
- HANDOFF
- 5 professional artifacts · statistics runtime
SQL5
Python4
Data complexity5
Statistical reasoning5
Decision ambiguity5
Deliverable load5
The Queue Nobody Owns
Design a safe shadow-routing system for uncategorized support work.
- SQL CORE
- Construct leak-resistant conversation-level labels and train/validation cohorts.
- PYTHON CORE
- Build TF-IDF and comparison pipelines, inspect class errors, add abstention, package inference, and define monitoring.
- PREREQUISITES
- Text representation · Classification · Error analysis · Model packaging
- HANDOFF
- 8 professional artifacts · nlp runtime
SQL4
Python5
Data complexity4
Statistical reasoning4
Decision ambiguity5
Deliverable load5
Too Good to Ship
Audit an implausibly strong cancellation model and decide its smallest safe path forward.
- SQL CORE
- Trace model lineage, snapshot grain, labels, and point-in-time availability.
- PYTHON CORE
- Reproduce leakage, compare grouped temporal validation, build an allow-listed baseline, and audit calibration and segments.
- PREREQUISITES
- Model validation · Temporal splits · Calibration · Data governance
- HANDOFF
- 5 professional artifacts · modeling runtime
SQL3
Python5
Data complexity5
Statistical reasoning5
Decision ambiguity5
Deliverable load5
ASSESSMENT BOUNDARY
Machines verify mechanics. Instructors evaluate judgment.
Execution, artifact presence, declared schemas, hashes, and exact scenario-authored invariants may be verified. Metric quality, causal language, modeling choices, uncertainty, and recommendations remain human-reviewed.