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PostgreSQL Summary Stats and Window Functions

IntermediateSkill Level
4.7+
6,644 reviews
Updated 01/2026
Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!
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SQLProgramming4 hr12 videos44 Exercises3,550 XP120K+Statement of Accomplishment

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Course Description

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.

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What you'll learn

  • Assess the use of aggregate window functions with frame clauses to compute running totals, moving averages, and other cumulative statistics
  • Differentiate among core window functions such as ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, FIRST_VALUE, LAST_VALUE, and NTILE based on their operational behavior and analytic applications
  • Distinguish between ROWS and RANGE frame types and Evaluate their effects on result sets when ordering columns contain duplicate values
  • Identify the essential syntax elements of PostgreSQL window functions, including the OVER clause, ORDER BY, PARTITION BY, and frame definitions
  • Recognize advanced data-reshaping and summarization techniques using CROSSTAB pivoting, ROLLUP, and CUBE to generate multi-level totals in PostgreSQL analyses.

Prerequisites

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.
Start Chapter
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.
Start Chapter
3

Aggregate window functions and frames

4

Beyond window functions

PostgreSQL Summary Stats and Window Functions
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*4.7
from 6,644 reviews
81%
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  • Michael
    2 hours ago

  • Carlos
    4 hours ago

    Todo bien ya al final se hace algo dificil pero nada que no se pueda si recomiendo tomar breaks despues del tercer capitulo

  • Daniel
    6 hours ago

  • Melissa
    6 hours ago

  • Martin
    6 hours ago

  • Renan
    9 hours ago

Michael

"Todo bien ya al final se hace algo dificil pero nada que no se pueda si recomiendo tomar breaks despues del tercer capitulo"

Carlos

Daniel

FAQs

What real-world problems can I solve after this course?

You will be able to generate rankings, calculate running totals and moving averages, deduplicate data, and perform time intelligence queries for analytics and data engineering tasks.

What dataset will I work with?

You will practice on flights data, using it to explore how window functions handle ranking, partitioning, and cumulative calculations in realistic scenarios.

What database system is used in this course?

The course uses PostgreSQL, covering syntax specific to its window functions, frame definitions, and advanced features like CROSSTAB, ROLLUP, and CUBE.

Who will benefit from this course?

Data analysts, analytics engineers, and data engineers who already write basic SQL and want to handle more advanced reporting and transformation tasks.

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