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SQL for Data Analytics

You're reading from  SQL for Data Analytics

Product type Book
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Pages 386 pages
Edition 1st Edition
Languages
Authors (3):
Upom Malik Upom Malik
Profile icon Upom Malik
Matt Goldwasser Matt Goldwasser
Profile icon Matt Goldwasser
Benjamin Johnston Benjamin Johnston
Profile icon Benjamin Johnston
View More author details

Table of Contents (11) Chapters

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics 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 how to use SQL to prepare datasets for analysis. Once the data is prepared, the next step is to analyze the data. Generally, data scientists and analytics professionals will try to understand the data by summarizing it and trying to find high-level patterns in the data. SQL can help with this task primarily through the use of aggregate functions: functions that take rows as input and return one number for each row. In this chapter, we will discuss how to use basic aggregate functions and how to derive statistics and other useful information from data using aggregate functions with GROUP BY. We will then use the HAVING clause to filter aggregates and see how to clean data and examine data quality using aggregate functions. Finally, we look at how to use aggregates to understand data quality

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