🟦 Week 1: Introduction + Excel & Business Foundations What Data Analysts do across industries Advanced Excel (Pivot Tables, VLOOKUP/XLOOKUP, dashboards) KPI definitions, metrics, A/B testing basics Intro to healthcare and finance datasets
🟦 Week 2: SQL Basics – The Core of Analysis Relational database concepts (schemas, ERDs) SELECT, WHERE, JOINs, GROUP BY, ORDER BY Aggregation & filtering Mini project: Build queries to calculate churn, claims costs, or refunds
🟦 Week 3: Advanced SQL + Use Cases Subqueries, CTEs, CASE WHEN, NULL handling Window functions: RANK, DENSE_RANK, LAG, LEAD Real-world use case: Analyze card transactions, patient costs, or quality measures SQL Performance tuning intro
🟦 Week 4: Power BI (or Tableau) – Data Visualization Importing and modeling data Building reports with slicers, filters, DAX functions Visual storytelling: designing dashboards for finance or healthcare Project: Build an executive dashboard using mock data
🟦 Week 5: Power BI (Advanced) + Dashboard Capstone Measures vs. calculated columns KPI indicators, bookmarks, drill-throughs Publishing dashboards and real-time updates Final Power BI dashboard project
🟦 Week 6: Python Essentials for Analysts Python basics (variables, loops, functions) Pandas: Reading/writing data, filtering, aggregating Numpy for arrays and basic computations Python vs SQL: what to use when
🟦 Week 7: Python with Pandas + Visualization GroupBy, pivoting, reshaping Cleaning & merging messy datasets Matplotlib & Seaborn for data charts Mini project: Claims cost trend or credit usage visualization