Advanced SQL for Data Analytics Course

Course Objectives

By the end of this course, participants will be able to:

• Write complex SQL queries to perform in-depth data analysis.
• Utilize advanced SQL functions and techniques for data manipulation and transformation.
• Optimize SQL queries for performance and efficiency in large datasets.
• Implement window functions to perform advanced analytical calculations.
• Work with various SQL data types and convert between them effectively.
• Use SQL for data cleaning, aggregation, and reporting.
• Apply SQL in real-world data analysis scenarios.
• Create and manage indexes to improve query performance.
• Integrate SQL with other data analysis tools and platforms.
• Develop strategies for handling large datasets and optimizing storage in SQL databases.

Course Outline:

Module 1: SQL Performance Optimization

• Query execution plans and performance analysis
• Indexing strategies and techniques
• Query rewriting and simplification
• Partitioning and sharding for large datasets

Module 2: Advanced Data Modeling

• Dimensional and snowflake schemas
• Star and fact tables
• Data warehousing concepts
• Data modeling for analytics and reporting

Module 3: Window Functions and Analytical Queries

• Ranking and partitioning data
• Calculating running totals, moving averages, and cumulative sums
• Lead and lag functions for data analysis
• Advanced window functions for complex calculations

Module 4: Recursive Queries and Common Table Expressions (CTEs)

• Hierarchical data and recursive queries
• Creating and using CTEs for complex logic
• Recursive CTEs for tree-like structures

Module 5: Regular Expressions and Pattern Matching

• Regular expression syntax and functions
• Text mining and information extraction
• Data cleaning and standardization using regular expressions

Module 6: Advanced Joins and Set Operations

• Beyond inner and outer joins
• Full outer joins and self-joins
• Set operations (UNION, INTERSECT, EXCEPT)
• Advanced join optimization

Module 7: Subqueries and Correlated Subqueries

• Nested queries and correlated subqueries
• EXISTS and NOT EXISTS operators
• IN and ANY/ALL operators

Module 8: SQL for Data Analysis and Business Intelligence

• Creating reports and dashboards
• Data visualization with SQL
• Predictive analytics with SQL
• SQL for machine learning

Module 9: SQL for Big Data

• SQL on Hadoop and Spark
• Distributed SQL processing
• Handling large datasets with SQL

Module 10: Case Studies and Real-world Applications

• In-depth analysis of real-world datasets
• SQL for fraud detection, customer segmentation, and churn analysis
• Performance optimization case studies
• Data modeling and warehousing projects