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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

In the previous chapters, you learned the basics of data analysis and SQL. You learned how to use CREATE, INSERT, SELECT, ALTER, UPDATE, DELETE, and DROP SQL statements to apply create, read, update, and delete (CRUD) operations on a table. These techniques are the foundation for data analytics.

However, in the real world, as a data analyst, you usually do not handle the entire CRUD flow. To be more specific, you usually do not create datasets from scratch. You will receive data from outside sources. This data is usually in a form that would not fit your needs perfectly and you would need to perform some transform operations to make the data usable. One such operation is the creation of clean datasets from existing raw datasets. The raw data may be missing some information, contain information that is not in the format that fits your needs, or contains information that may not be accurate.

According to Forbes, it is estimated that almost 80% of the time spent...

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