Data Analytics & Business Intelligence Bootcamp Certificate

Checkpoint 1: Choose a real business question and dataset; define grain, key entities, metric formulas, decision owner and what evidence would change the decision.
Checkpoint 2: Load and query the data in SQL; build joins, aggregations and reusable queries while checking duplicates, missingness and grain mismatches.
Checkpoint 3: Reconcile or explore the same dataset in Excel; use formulas/pivots or Power Query where useful and document any discrepancies against SQL.
Checkpoint 4: Build a Tableau or Power BI dashboard with clear hierarchy, filters and metric definitions; remove visual clutter and test whether the key decision is obvious.
Checkpoint 5: Use Python/pandas for a cleaning, enrichment or analysis step that is cumbersome in SQL/Excel; make the transformation reproducible and validate outputs.
Checkpoint 6: Publish a portfolio-ready case study with query/code evidence, dashboard screenshots/link, data-quality notes, insights, limitations and an executive recommendation.

Day 1: Frame the business problem, stakeholders, decision, dataset, metric dictionary and success criteria; establish raw-data snapshot and assumptions.
Day 2: Profile data and build SQL analysis tables/queries; document grain, joins, duplicates, nulls and reconciliation checks.
Day 3: Use Excel for targeted exploratory analysis or stakeholder-friendly validation; compare totals and investigate mismatches.
Day 4: Create the first Tableau/Power BI dashboard; test chart choices, filters, drill paths and whether each visual supports the stated decision.
Day 5: Add a Python/pandas pipeline for cleaning/enrichment or deeper analysis; export a reproducible curated dataset and validate against source totals.
Day 6: Add a cloud or modern-platform step where useful (AWS/Databricks or equivalent), finalize dashboard/story and conduct an error/assumption review.
Day 7: Publish portfolio case study, SQL/code, metric dictionary, dashboard, QA checklist, demo video and a one-page decision memo with recommendation and caveats.