Window Functions Practice Sheet – 150+ Real-World Problems Across 12 Business Domains
Window functions are the backbone of modern SQL analytics — yet they are often learned superficially.
This practice sheet is designed to help you master window functions through deep, real-world analytical problem-solving, not isolated syntax examples.
You’ll work through 12 comprehensive assignments, each focused on a distinct business domain.
Each assignment contains ~10–15 analytical questions, resulting in 150+ problems covering beginner to expert-level use cases.
What this practice sheet includes
12 industry-focused SQL analytics assignments:
- Movie Analytics
- Indian E-commerce Analytics
- Indian Hospital Analytics
- Indian Tea Production Analytics
- Indian School Analytics
- Indian Bus Transport Analytics
- Indian OTT Streaming Platform Analytics
- Indian Retail Chain Analytics
- E-commerce Analytics (Part 2)
- Amazon India Fulfilment Centre Analytics
- Startup Investment Platform Analytics
- Global Retail Sales Analytics
Each assignment mirrors real-world business analytics tasks, including ranking, trend analysis, comparisons, cumulative metrics, and performance analysis.
Concepts you’ll practice extensively
- ROW_NUMBER, RANK, DENSE_RANK, NTILE
- Running totals & moving averages
- PARTITION BY & ORDER BY analytics
- LEAD / LAG for trend and gap analysis
- Window-based aggregates (SUM, AVG, COUNT)
- Percentiles and distribution analysis (PERCENT_RANK(), CUME_DIST())
- Complex, multi-step analytical logic
Recommended Roadmap
Step 1: Start sequentially (Assignments 1–4)
Build a strong foundation with ranking, basic aggregates, and simple partitions.
Step 2: Move to intermediate analytics (Assignments 5–9)
Focus on moving averages, trends, comparisons, and business KPIs.
Step 3: Master advanced analytics (Assignments 10–12)
Solve expert-level problems involving logistics, finance, global sales, and multi-dimensional analytics.
This progression ensures conceptual depth, not surface-level familiarity.
How You’ll Practice
- Each assignment provides a full table setup (DDL & DML)
- Solve questions independently before checking patterns
- Focus on why a window function is used, not just which
- Rewrite solutions using alternate window frames
- Optimize queries for clarity and performance
- Revisit complex assignments after completing simpler ones
This approach trains you to think like a data analyst, not just write queries.
</li><li>Amazon India Fulfilment Centre Analytics</li><li>Startup Investment Platform Analytics</li><li>Global Retail Sales Analytics</li></ol><p><br></p><p>Each assignment mirrors <strong>real-world business analytics tasks,</strong> including ranking, trend analysis, comparisons, cumulative metrics, and performance analysis.</p><p><br></p><h2><strong>Concepts you’ll practice extensively</strong></h2><ul><li>ROW_NUMBER, RANK, DENSE_RANK, NTILE</li><li>Running totals & moving averages</li><li>PARTITION BY & ORDER BY analytics</li><li>LEAD / LAG for trend and gap analysis</li><li>Window-based aggregates (SUM, AVG, COUNT)</li><li>Percentiles and distribution analysis (<span style="color: rgb(0, 0, 0); background-color: transparent;">PERCENT_RANK(), CUME_DIST())</span></li><li>Complex, multi-step analytical logic</li></ul><p><br></p><h2><strong>Recommended Roadmap</strong></h2><p><strong>Step 1: Start sequentially (Assignments 1–4)</strong></p><p>Build a strong foundation with ranking, basic aggregates, and simple partitions.</p><p><strong>Step 2: Move to intermediate analytics (Assignments 5–9)</strong></p><p>Focus on moving averages, trends, comparisons, and business KPIs.</p><p><strong>Step 3: Master advanced analytics (Assignments 10–12)</strong></p><p>Solve expert-level problems involving logistics, finance, global sales, and multi-dimensional analytics.</p><p>This progression ensures <strong>conceptual depth, not surface-level familiarity</strong>.</p><p><br></p><h2><strong>How You’ll Practice</strong></h2><ul><li>Each assignment provides a full table setup (DDL & DML)</li><li>Solve questions independently before checking patterns</li><li>Focus on <em>why</em> a window function is used, not just <em>which</em></li><li>Rewrite solutions using alternate window frames</li><li>Optimize queries for clarity and performance</li><li>Revisit complex assignments after completing simpler ones</li></ul><p><br></p><p>This approach trains you to <strong>think like a data analyst</strong>, not just write queries.</p>&redirect_uri=http://www.facebook.com)
West Bengal, India
