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SQL for Data Analytics

You're reading from  SQL for Data Analytics

Product type Book
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Pages 386 pages
Edition 1st Edition
Languages
Authors (3):
Upom Malik Upom Malik
Profile icon Upom Malik
Matt Goldwasser Matt Goldwasser
Profile icon Matt Goldwasser
Benjamin Johnston Benjamin Johnston
Profile icon Benjamin Johnston
View More author details

Table of Contents (11) Chapters

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics 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 we understand how window functions work, we can start using them to calculate useful statistics, such as ranks, percentiles, and rolling statistics.

In the following table, we 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 5.10: Statistical window functions

Exercise 17: Rank Order of Hiring

ZoomZoom would like to promote salespeople at their regional dealerships to management and would like to consider tenure in their decision. Write a query that will rank the order of users according to their hire date for each dealership:

  1. Open your favorite SQL client and connect to the sqlda database.
  2. Calculate a rank for every salesperson, with a rank of 1 going to the first hire, 2 to the second hire, and so on, using the RANK() function:
    SELECT *,...
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