Write correct SQL across all seven B-topics under time pressure — filtering, aggregation, joins, subqueries/CTEs, window functions, date logic.
Clean a dataset with real defects, run a structured EDA on unseen data, and correctly run and interpret a hypothesis test — including recognising a non-significant result.
Build a working Power BI model with correct relationships and DAX measures, then ship a one-screen exec dashboard that answers a real question.
Write a stakeholder memo that leads with the recommendation, and catch a hallucinated table, a mis-attributed figure, and a grain bug in AI-drafted analysis before it reaches a stakeholder.
Pass a 3-hour timed practical on a fresh dataset, defend the capstone live, and ship a portfolio ready to show an employer.
Every lab: README, 4–6 exercises (Guided → Applied → Stretch), self-check, answer key, honest time estimate.
| Module | Format | Time |
|---|---|---|
| A1 What an Analyst Actually Does | Written exercise + rubric | 45 min |
| A2 Spreadsheets for Analysis | Google Sheets / Excel | 90 min |
| A3 Descriptive Statistics | Google Sheets / Excel | 60 min |
| A4 Data Literacy & Metric Design | Written + discussion | 60 min |
| Module | Format | Time |
|---|---|---|
| B1 SELECT, WHERE, ORDER BY | Browser SQL | 60 min |
| B2 Aggregation & GROUP BY | Browser SQL | 75 min |
| B3 Joins | Browser SQL | 90 min |
| B4 Subqueries & CTEs | Browser SQL | 90 min |
| B5 Window Functions | Browser SQL | 90 min |
| B6 Date & Time Analysis | Browser SQL | 75 min |
| B7 Business Metrics in SQL | Browser SQL | 90 min |
| Module | Format | Time |
|---|---|---|
| C1 Python Essentials | Colab notebook | 90 min |
| C2 pandas Fundamentals | Colab notebook | 90 min |
| C3 Cleaning Messy Data | Colab, orders_dirty.csv | 90 min |
| C4 Exploratory Data Analysis | Colab notebook | 90 min |
| C5 Visualisation in Python | Colab notebook | 75 min |
| C6 Statistics & A/B Testing | Colab notebook | 90 min |
| Module | Format | Time |
|---|---|---|
| D1 Visual Design Principles | Critique exercise | 45 min |
| D2 Power BI Fundamentals | Power BI Desktop | 90 min |
| D3 DAX & Measures | Power BI Desktop | 90 min |
| D4 Dashboard Design & Storytelling | Power BI Desktop | 120 min |
| Module | Format | Time |
|---|---|---|
| E1 Metrics & North Stars | Driver-tree exercise + rubric | 60 min |
| E2 Communicating to Stakeholders | Exec summary, peer-reviewed | 60 min |
| E3 Case Interview Frameworks | 6 cases with rubrics | 90 min |
| E4 Domain Primer: BFSI & E-commerce | Browser SQL | 75 min |
| E5 AI-Assisted Analysis | 5 written exercises + rubric | 75 min |
| Module | Format | Time |
|---|---|---|
| F1 SQL Interview Drills | Timed browser SQL, 6 drills | 45 min |
| F2 Case Study Practice | 12 written cases, rubrics | 6 × 60 min |
| F3 Take-Home Simulation | 4-hr brief, graded | 4 hrs |
| F4 Capstone | SQL + Python + dashboard + readout | 20+ hrs |
| F5 Portfolio & Positioning | GitHub, resume, LinkedIn draft | 4 hrs |
| F6 Final Assessment | 3-hr timed practical, pass/fail | 3 hrs |
A fictional Indian D2C skincare brand. Every figure quoted in the course is machine-verified — nothing from memory.
SQL fluency across 7 topics — filtering, aggregation, joins, CTEs, window functions, date logic, business metrics — re-tested cold under time pressure.
A full Python/pandas analysis — cleaned a genuinely dirty dataset, ran a structured EDA, built four chart types, and ran a hypothesis test correctly.
A Power BI dashboard — a working data model with correct DAX measures, shipped as a one-screen exec dashboard answering a real question.
A written stakeholder memo — a 200-word, pyramid-principle memo recommending where to move a real marketing budget.
AI-tool verification discipline — catching hallucinated tables, mis-attributed figures, and grain bugs before they reach a stakeholder.
18 case-interview reps — revenue diagnostics, channel strategy, guesstimates, conflicting-metrics judgement calls.
A capstone GitHub repo — SQL, Python, Power BI dashboard, a 5-slide readout, README, resume bullets, LinkedIn draft.
A pass/fail credential — a 3-hour timed practical on a dataset never seen before.
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