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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

Using Aggregates to Clean Data and Examine Data Quality

In Chapter 3, SQL for Data Preparation, you learned how SQL can be used to clean data. While the techniques mentioned in that chapter do an excellent job of cleaning data, aggregates add a number of techniques that can make cleaning data even easier and more comprehensive. In this section, you will look at some of these techniques.

Finding Missing Values with GROUP BY

As mentioned in Chapter 3, SQL for Data Preparation, one of the biggest issues with cleaning data is dealing with missing values. You learned how to find missing values and how to resolve this issue. In this chapter, you will learn how to determine the extent of missing data in a dataset.

Using aggregates, identifying the amount of missing data can tell you not only which columns have missing data but also the usability of the columns when so much of the data is missing. Depending on the extent of missing data, you will have to determine whether it...

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