SQL for Data Analysis: Advanced SQL Querying Techniques
Learn advanced data analysis with SQL, and master topics like subqueries, CTEs, window functions, and more
Product Brand: Udemy
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Udemy Coupon Code for SQL for Data Analysis: Advanced SQL Querying Techniques Course. Learn advanced data analysis with SQL, and master topics like subqueries, CTEs, window functions, and more
Created by Maven Analytics, Alice Zhao | 8.5 hours on-demand video course | 2 downloadable resources
SQL for Data Analysis Course Overview
SQL for Data Analysis: Advanced SQL Querying Techniques
This is a hands-on, project-based course designed to help you move beyond the “Big 6” clauses into advanced querying techniques. We’ll start by reviewing the basics and conducting multi-table analyses, including basic joins, self-joins, cross-joins, and unions.
Next, we’ll cover different ways of working with nested queries by writing subqueries and common table expressions, or CTEs. We’ll walk through examples of subqueries within the various clauses, rewrite subqueries as CTEs, introduce recursive CTEs, and compare these techniques to other options like temporary tables and views.
What you’ll learn
- Conduct multi-table analysis using JOINs and learn variations like self joins, cross joins, and more
- Learn to work with nested queries by writing subqueries and common table expressions (CTEs), and understand the best use cases for each
- Use window functions to perform calculations across a set of rows and learn various function options and applications
- Discover the many SQL functions that can be applied to fields of numeric, datetime, string, and NULL data types
- Apply advanced querying techniques to common data analysis scenarios, including pivoting data, rolling calculations, and more
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Who this course is for:
- Analysts or BI professionals looking to analyze data stored in relational database systems
- SQL users who want to develop advanced querying skills
- Anyone looking for a hands-on, practical, and highly engaging way to master SQL for advanced data analysis