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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
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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

Aggregate Functions

In addition to just seeing individual rows of data, it is also interesting to understand the properties of an entire column or table. For example, say you just received a sample dataset of a fictional company called ZoomZoom, which specializes in car and electronic scooter retailing. You are wondering about the number of customers that this ZoomZoom database contains. You could select all the data from the table and then see how many rows were pulled back, but it would be incredibly tedious to do so. Luckily, there are functions provided by SQL that can be used to perform this type of calculation on large groups of rows. These functions are called aggregate functions.

Aggregate functions take in one or more columns with multiple rows and return a number based on those columns. The following table provides a summary of the major aggregate functions that are used in SQL:

Figure 4.1: Major aggregate functions

The most frequently used...

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