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SQL for Data Analytics

You're reading from   SQL for Data Analytics Harness the power of SQL to extract insights from data

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801812870
Length 540 pages
Edition 3rd Edition
Languages
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Authors (4):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Jun Shan Jun Shan
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Jun Shan
Upom Malik Upom Malik
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Upom Malik
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Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics FREE CHAPTER 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth: A Case Study Appendix

Statistics with Window Functions

Now that you understand how window functions work, you can start using them to calculate useful statistics, such as ranks, percentiles, and rolling statistics.

In the following table, you have summarized a variety of statistical functions that are useful. It is also important to emphasize again that all aggregate functions can also be used as window functions (AVG, SUM, COUNT, and so on):

Figure 5.12: Statistical window functions

Normally, a call to any of these functions inside a SQL statement would be followed by the OVER keyword. This keyword will then be followed by more keywords like PARTITION BY and ORDER BY, either of which may be optional, depending on which function you are using.

For example, the ROW_NUMBER() function will look like this:

ROW_NUMBER() OVER(
  PARTITION BY column_1, column_2
  ORDER BY column_3, column_4
)

You will practice how to use these statistical functions in the...

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