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SQL for Data Analytics

You're reading from   SQL for Data Analytics Analyze data effectively, uncover insights and master advanced SQL for real-world applications

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Product type Paperback
Published in Nov 2025
Publisher Packt
ISBN-13 9781836646259
Length 336 pages
Edition 4th Edition
Languages
Tools
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Authors (5):
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Jun Shan Jun Shan
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Jun Shan
Haibin Li Haibin Li
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Haibin Li
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Upom Malik Upom Malik
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Upom Malik
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1: Data Management Systems
2. Introduction to Data Management Systems FREE CHAPTER 3. Creating Tables with Solid Structures 4. Exchanging Data Using COPY 5. Manipulating Data with Python 6. Part 2: Data Presentation and Manipulation
7. Presenting Data with SELECT 8. Transforming and Updating Data 9. Defining Datasets from Existing Datasets 10. Aggregating Data with GROUP BY 11. Inter-Row Operation with Window Functions 12. Part 3: Advanced Topics on Analytics
13. Performant SQL 14. Processing JSON and Arrays 15. Advanced Data Types: Date, Text, and Geospatial 16. Inferential Statistics Using SQL 17. A Case Study for Analytics Using SQL 18. Unlock Your Exclusive Benefits 19. Other Books You May Enjoy
20. Index

A Case Study for Analytics Using SQL

So far in this book, you have learned about the various features and functionalities offered by SQL. Now, if you take a step back and think about the fundamental question of “why” (i.e., why do you need these features and functionalities at all), you will see that it all stems from the need for data analysis.

Data originates in individual activities. Each activity creates its own data point and context. When the data of all the activities in a certain domain is collected and grouped together, statistical patterns emerge. As discussed from the very first chapter of this book, ancient Egyptian pharaohs collected census information to determine the size of the army they could recruit, and modern companies dive into sales data to find the most attractive products for promotion. All these involve the same process of gathering activity level data (the number of people in each household, or the quantity and amount of each sales transaction...

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