Learn advanced data analysis with SQL, and master topics like subqueries, CTEs, window functions, and more

SQL for Data Analysis: Advanced SQL Querying Techniques

Name: Complex Querying via Targeted Practical Application
Rating: 6.4

InstructorMaven Analytics (2,000,000+ Learners)

Duration8h 21m

Students47.3K

Rating4.6 (8,332)

Complex Querying via Targeted Practical Application

Strengths

Practical Application Focus

The curriculum utilizes targeted assignments and a multi-part final project to reinforce complex querying concepts like pivoting and rolling calculations.

Best suited to

Data analysts seeking practical SQL applications
Intermediate users looking to master window functions and CTEs

Less suited to

Self-taught beginners needing a gentler difficulty curve
Learners expecting highly advanced theoretical depth

Editorial course preview

What the public course preview actually shows

These 4 complementary views highlight concrete, legible examples from the course presentation.

A presentation slide titled PREVIEW: FINAL PROJECT outlining a Data Analyst Intern scenario for Major League Baseball, listing objectives like tracking player stats and team salary — Preview 1 of 4
This slide outlines the final project scenario where students act as Data Analyst Interns for MLB to analyze historical player data using advanced SQL techniques.

A screenshot of a SQL code editor displaying a query using window functions to calculate cumulative spending, with a result grid below showing team IDs, years, and calculated spend — Preview 2 of 4
This screenshot shows a SQL query using window functions to calculate cumulative spending by team over the years, with the resulting data displayed in a grid below.

An educational slide titled WINDOW FUNCTION COMPONENTS breaks down SQL syntax with arrows pointing to ROW_NUMBER, OVER, PARTITION BY, and ORDER BY clauses. — Preview 3 of 4
This instructional slide details the four main components of window functions in SQL, using a specific example to illustrate how to partition and order data.

A presentation slide titled ROLLING CALCULATIONS showing two tables of pizza order data with a blue arrow labeled Subtotals pointing from the raw data to the aggregated results, fl — Preview 4 of 4
This slide illustrates rolling calculations by comparing a raw dataset of pizza orders against a table of subtotals and cumulative sums for each customer.

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SQL for Data Analysis: Advanced SQL Querying Techniques