PostgreSQL Summary Stats and Window Functions
Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!
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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.
- 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.
Introduction50 xpWindow functions vs GROUP BY50 xpNumbering rows100 xpNumbering Olympic games in ascending order100 xpORDER BY50 xpNumbering Olympic games in descending order100 xpNumbering Olympic athletes by medals earned100 xpReigning weightlifting champions100 xpPARTITION BY50 xpReigning champions by gender100 xpReigning champions by gender and event100 xpRow numbers with partitioning50 xp - 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.
- 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 functions50 xpRunning totals of athlete medals100 xpMaximum country medals by year100 xpMinimum country medals by year100 xpFrames50 xpNumber of rows in a frame50 xpMoving maximum of Scandinavian athletes' medals100 xpMoving maximum of Chinese athletes' medals100 xpMoving averages and totals50 xpMoving average's frame50 xpMoving average of Russian medals100 xpMoving total of countries' medals100 xp - 4
Beyond window functions
In this last chapter, you'll learn some techniques and functions that are useful when used together with window functions.
Datasets
Summer olympics datasetCollaborators
Prerequisites
Data Manipulation in SQLMichel Semaan
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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.
Fernando Gonzalez Prada
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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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- Krzysztof Z.7 days
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- Agnius M.12 days
I liked it
- Amy U.21 days
It's a great course for learning the fundamentals and details of window functions. The course goes more profound than the few commonly mentioned WFs which is great.
- Lynn P.22 days
I thought the entire sql track was fantastic, it had just the right amount of challenge and learning to make it effective
- Paweł Ż.23 days
Great training!
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