Data Analytics
Lab Curriculum.

BUG FOUND: analysts who know tools but cannot trust a dashboard built on dirty data.

$ step-01 brushup // weeks 1–2 — Topics taught:

1. SQL: joins, aggregations, window functions, and common join mistakes.
2. Excel: cleaning, lookup, pivot, and formula auditing.
3. Power BI / dashboard basics: measures, filters, and chart choice.
4. Data types, dates, and currency handling — where silent errors creep in.
5. Exploratory analysis: distributions, outliers, and missing values.
6. Writing a metric definition so two analysts get the same number.

$ step-02 debug // weeks 3–4 — Production Debugging. Tools: SQL, Excel, Power BI, a sample warehouse.

• BUG #1: Dashboard revenue is double-counted — find the fan-out join, fix it, reconcile to source totals.
• BUG #2: A KPI changed overnight with no business change — trace it to a date-filter / timezone error, fix it, document the cause.
• BUG #3: Duplicate rows inflate a count — find the duplicate key, deduplicate correctly, show before/after counts.
• BUG #4: Currency conversion is wrong for one region — find the rate / rounding error, fix it, verify against source.

$ step-03 navigate // weeks 5–7 — Codebase / System Navigation:

• Trace a metric definition to its source tables across a messy warehouse; document lineage in a data dictionary.
• Fix an upstream logic error (e.g. wrong status mapping) and prove downstream dashboards change correctly.
• Reconcile three reports that disagree on the same metric; explain which is right and why.
• Build a data-quality checklist (nulls, duplicates, ranges, freshness) and apply it to a new dataset.

$ step-04 ship // weeks 8–9 — Shipping Features: capstone — a decision-ready dashboard answering a real business question, with a written insight note, limitations, and next actions.

$ ship → PROOF — exact output: dashboard link/file, cleaned dataset, a metric-definition document, and a one-page insight note with limitations stated.

$ run --lab data-analytics → ✓ SHIPPED, job-ready