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SQL for Data Analytics

You're reading from   SQL for Data Analytics Perform fast and efficient data analysis with the power of SQL

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Length 386 pages
Edition 1st Edition
Languages
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Authors (3):
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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
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 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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