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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

Introduction

You have learned simple functions such as CASE WHEN, COALESCE, and NULLIF in Chapter 3, SQL for Data Preparation. These functions receive data from a single row and produce a result for this row. The result of these functions is only determined by the data value in the row and has nothing to do with the dataset it is in. You have also learned aggregate functions such as SUM, AVG, and COUNT in Chapter 4, Aggregate Functions for Data Analysis. These functions receive data from a dataset of multiple rows and produce a result for this dataset. Both types of functions are useful in different scenarios. For example, if you have the physical checkup results of all newborn babies in a country, such as weight and height, you can check each baby's health by checking these measurements to be within a given range using CASE WHEN function. You can also use aggregate functions to get the average and standard deviation of the weight and height of babies in this country. Both types...

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