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PostgreSQL Summary Stats and Window Functions

MellanliggandeFärdighetsnivå

Uppdaterad 2026-01

Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!

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SQLProgramming4 timmar12 videos44 exercises3,550 XP120K+Uttalande om prestation

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Kursbeskrivning

Have you ever wondered how data professionals use SQL to solve real-world business problems, like generating rankings, calculating moving averages and running totals, deduplicating data, or performing time intelligence? If you already know how to select, filter, order, join and group data with SQL, this course is your next step. By the end, you will be writing queries like a pro! You will learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! Using flights data, you will discover how simple it is to use window functions, and how flexible and efficient they are.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos. The course glossary can be found on the right in the resources section. To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

Förkunskapskrav

Data Manipulation in SQL

1

Introduction to window functions

In this chapter, you'll learn what window functions are, and the two basic window function subclauses, ORDER BY and PARTITION BY.

Introduction

Window functions vs GROUP BY

Numbering rows

Numbering Olympic games in ascending order

Numbering Olympic games in descending order

Numbering Olympic athletes by medals earned

Reigning weightlifting champions

PARTITION BY

Reigning champions by gender

Reigning champions by gender and event

Row numbers with partitioning

2

Fetching, ranking, and paging

In this chapter, you'll learn three practical applications of window functions: fetching values from different parts of the table, ranking rows according to their values, and binning rows into different tables.

Future gold medalists

First athlete by name

Last country by name

Ranking athletes by medals earned

Ranking athletes from multiple countries

DENSE_RANK's output

Paging events

Top, middle, and bottom thirds

3

Aggregate window functions and frames

In this chapter, you'll learn how to use aggregate functions you're familiar with, like AVG() and SUM(), as window functions, as well as how to define frames to change a window function's output.

Aggregate window functions

Running totals of athlete medals

Maximum country medals by year

Minimum country medals by year

Number of rows in a frame

Moving maximum of Scandinavian athletes' medals

Moving maximum of Chinese athletes' medals

Moving averages and totals

Moving average's frame

Moving average of Russian medals

Moving total of countries' medals

4

Beyond window functions

In this last chapter, you'll learn some techniques and functions that are useful when used together with window functions.

A basic pivot

Pivoting with ranking

ROLLUP and CUBE

Country-level subtotals

All group-level subtotals

A survey of useful functions

Cleaning up results

Summarizing results

PostgreSQL Summary Stats and Window Functions

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