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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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Toc

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

Summary

In this chapter, you learned how to calculate the statistical properties of a dataset using aggregate functions, such as the average, count, minimum, maximum, and standard deviation. Aggregate functions themselves are applied to a whole dataset. In order to use them to analyze the statistics of sub-datasets inside a larger dataset, you also learned about the GROUP BY clause of the SELECT statement, which divides a large dataset into smaller ones based on the keys you provided and applies aggregate functions to each of the groups.

To make the GROUP BY clause more useful, several additional properties were introduced, most importantly the HAVING clause. This HAVING clause is used to filter the values of aggregated groups. It is applied at the second stage of the GROUP BY clause execution and should be distinguished from the WHERE clause, which is applied to the original data table or table set and is applied at the first stage of the GROUP BY execution.

Now that you learned...

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