PostgreSQL Summary Stats and Window Functions
Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!
- 4 hours
- 12 Videos
- 44 Exercises
- 6,271 Participants
- 3,550 XP
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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.
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1
Introduction to window functions
FreeIn this chapter, you'll learn what window functions are, and the two basic window function subclauses, ORDER BY and PARTITION BY.
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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.
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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.
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Beyond window functions
In this last chapter, you'll learn some techniques and functions that are useful when used together with window functions.
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1
Introduction to window functions
FreeIn this chapter, you'll learn what window functions are, and the two basic window function subclauses, ORDER BY and PARTITION BY.
-
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.
-
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.
-
Beyond window functions
In this last chapter, you'll learn some techniques and functions that are useful when used together with window functions.
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Michel Semaan
Data Scientist
Michel is a data scientist who's worked at leading Middle-Eastern startups like Bookwitty (e-commerce) and Anghami (music streaming). He loves using open-source technologies like Python, R, and SQL to analyze data, optimize KPIs, and solve problems.
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Fernando Gonzalez Prada
Data Science Consultant
Fernando is a data science professional with over 20 years of experience in databases, data integration, machine learning, and business intelligence. He designs analytics solutions with Microsoft Data Platform technologies and open source products. He has degrees in economics and IT certifications and is studying Business Analytics at Columbia University. Fernando loves teaching data science to beginners and helping experienced professionals who are transitioning to data science.
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Collaborators
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Mona Khalil
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Sumedh Panchadhar
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Marianna Lamnina