Learn advanced data analysis with SQL, and master topics like subqueries, CTEs, window functions, and more
InstructorMaven Analytics • 1,500,000 LearnersThe curriculum utilizes targeted assignments and a multi-part final project to reinforce complex querying concepts like pivoting and rolling calculations.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
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.
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.
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.
This slide illustrates rolling calculations by comparing a raw dataset of pizza orders against a table of subtotals and cumulative sums for each customer.
Instruction covers complex topics such as CTEs and window functions through clear, structured explanations that aid confidence in practical application.
One signal from before the displayed update notes a logic error in date conversion examples; others report friction regarding how data tables are introduced throughout the progression. Note that these signals predate the displayed update, and while the update label may have addressed them, it does not prove a correction.
One signal from before the displayed update notes a logic error in date conversion examples; others report friction regarding how data tables are introduced throughout the progression. Note that these signals predate the displayed update, and while the update label may have addressed them, it does not prove a correction.
The transition from basic to advanced content creates a significant gap that may not suit all self-taught learners.