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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

Introduction

In the previous chapter, we discussed aggregate functions, functions that can take a large group of rows and output a single value for them. Often, being able to summarize a group of rows to a single value is important and useful. However, there are times when you want to keep the individual rows as well as gaining a summarizing value. To do this, in this chapter, we will introduce a new set of functions named window functions, which can calculate aggregate statistics while keeping individual rows. These functions are very useful for being able to calculate new types of statistics, such as ranks and rolling averages, with relative ease within SQL. In this chapter, we will learn about what window functions are, and how we can use them to calculate statistics.

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