Overview of Advanced SQL
Stalwart Learning’s Advanced SQL Training for Data Science is designed to provide participants with the knowledge and skills necessary to work with complex datasets using SQL. This course is intended for data scientists, analysts, and database administrators who want to improve their SQL proficiency to extract insights and value from data.
In this course, participants will learn advanced SQL techniques to query, manipulate, and analyze data, including complex joins, subqueries, and window functions. They will also learn how to optimize SQL queries for performance and how to work with large datasets.
The course will cover real-world scenarios and case studies that demonstrate how SQL can be used for data analysis and reporting. Participants will gain hands-on experience working with SQL in a variety of data science contexts, such as machine learning, data visualization, and data modeling.
By the end of the course, participants will have a deeper understanding of SQL and the skills needed to work with complex datasets for data science applications. They will be equipped with the tools and techniques to leverage SQL as a powerful tool for data analysis and reporting.
Stalwart Learning’s Advanced SQL Training for Data Science is a must-have course for anyone looking to take their data science skills to the next level.
Duration
24 Hours
Module 1: Advanced Querying Techniques
- Complex SELECT statements with subqueries
- Joining multiple tables using INNER, OUTER, and CROSS joins
- Using set operators (UNION, INTERSECT, EXCEPT) for advanced data retrieval
- Working with nested queries and correlated subqueries
Module 2: Data Manipulation and Transactions
- Inserting, updating, and deleting data in tables
- Performing bulk operations with INSERT INTO SELECT
- Understanding transaction management and concurrency control
- Utilizing advanced transaction features such as SAVEPOINT and ROLLBACK
Module 3: Performance Optimization and Indexing
- Understanding query optimization and execution plans
- Identifying and resolving performance bottlenecks
- Creating and managing indexes for efficient data retrieval
- Using query hints and optimizer hints to improve performance
Module 4: Stored Procedures and Functions
- Creating and managing stored procedures
- Implementing user-defined functions (UDFs)
- Utilizing control flow and branching in stored procedures
- Passing parameters and handling errors in stored procedures
Module 5: Views and Materialized Views
- Creating and working with views for data abstraction
- Updating data through views and managing view dependencies
- Understanding materialized views for improved query performance
- Refreshing and maintaining materialized views
Module 6: Advanced Table Design and Constraints
- Implementing advanced table design techniques
- Working with composite keys and unique constraints
- Utilizing check constraints for data validation
- Using triggers for enforcing business rules and data integrity
Module 7: Window Functions and Analytical Queries
- Understanding window functions for advanced analytical queries
- Partitioning and ordering data within window functions
- Using ranking, aggregate, and statistical functions in window functions
- Applying window functions to solve complex analytical problems
Module 8: Advanced Data Types and Functions
- Working with advanced data types (arrays, JSON, XML)
- Utilizing built-in functions for data manipulation and analysis
- Handling date and time data with date functions
- Performing string manipulation and pattern matching
Module 9: Advanced Join Techniques
- Using advanced join techniques such as self-joins and outer joins
- Working with table aliases and derived tables
- Performing cross joins and Cartesian products
- Applying join conditions and filtering results
Module 10: Advanced SQL Topics and Best Practices
- Understanding SQL best practices for performance and maintainability
- Handling null values and NULL-related functions
- Implementing data validation and constraints
- Emerging trends and developments in SQL