Advanced SQL Training

Curriculum:

Module 1: Introduction to Advanced SQL for Analytics

• Overview of SQL in data analytics & BI
• Differences between intermediate and advanced SQL
• Analytical thinking with SQL: how SQL supports data-driven decisions

Module 2: Advanced Query Techniques

• Subqueries (correlated & non-correlated) in analytics
• Common Table Expressions (CTEs) for simplifying complex queries
• Recursive queries for hierarchical and organizational data
• Using CASE WHEN for advanced conditional logic

Module 3: Window (Analytic) Functions

• Introduction to window functions and their use in analytics
• Ranking functions: ROW_NUMBER(), RANK(), DENSE_RANK()
• Aggregate functions with OVER()
• Moving averages, running totals, and cumulative sums
• Partitioning and ordering datasets for time-series analytics

Module 4: Advanced Joins and Set Operations

• Complex join conditions and advanced use cases
• Self joins for hierarchical analysis
• FULL OUTER JOIN and CROSS JOIN in analytics
• Set operators: UNION, INTERSECT, EXCEPT in practice

Module 5: Aggregations and Grouping Techniques

• Advanced GROUP BY with ROLLUP, CUBE, GROUPING SETS
• Pivoting and unpivoting data in SQL
• Cohort and segmentation analysis using GROUPING techniques
• Case studies in financial and customer analytics

Module 6: Working with Dates, Time & Text for Analytics

• Advanced date and time functions for trend analysis
• Text functions for cleaning and preparing unstructured data
• Data enrichment techniques with SQL transformations

Module 7: Performance Optimization

• Query execution plans and optimization strategies
• Indexing best practices for analytics workloads
• Materialized views for performance improvement
• Reducing query runtime on large datasets

Module 8: Advanced Data Analysis with SQL

• Customer churn and retention analysis
• Sales performance and revenue trend analysis
• Funnel and conversion rate analysis
• Building reusable queries for BI dashboards

Module 9: Data Preparation and Transformation for Analytics

• Cleaning and transforming raw datasets with SQL
• Normalization vs. denormalization for analytics
• Data sampling and feature extraction using SQL

Module 10: Real-World Case Studies & Capstone Project

• Hands-on projects using real datasets (e.g., sales, e-commerce, finance, HR analytics)
• Building end-to-end analytics queries for BI dashboards
• Capstone Project: Create a comprehensive SQL analytics solution for a real-world dataset, covering performance optimization, advanced queries, and reporting outputs