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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

Summary

SQL provides you with many tools for mixing and cleaning data. In this chapter, you first learned how to combine two or more tables. You started with the JOIN keyword, which fuses data from tables based on their common columns. There are several types of JOIN. Depending on whether you want to retain the data in a certain table or not, you can choose INNER JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN, FULL OUTER JOIN, or CROSS JOIN. You then learned how to use subqueries and CTEs to preserve and reuse the results of queries. You can also use UNION and UNION ALL to merge the results of two queries with the same structure into one result set.

After learning how to combine data from different datasets, you learned how to perform certain transformations on the data. You first started with the CASE WHEN function, which is a generic way to convert one expression into another based on custom-defined conditions. You then learned how to use the COALESCE() and NULLIF() functions to convert...

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