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  • Book Overview & Buying SQL for Data Analytics
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SQL for Data Analytics

SQL for Data Analytics - Third Edition

By : Jun Shan, Matt Goldwasser , Upom Malik , Benjamin Johnston
4.8 (53)
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SQL for Data Analytics

SQL for Data Analytics

4.8 (53)
By: Jun Shan, Matt Goldwasser , Upom Malik , Benjamin Johnston

Overview of this book

Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.
Table of Contents (11 chapters)
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9
9. Using SQL to Uncover the Truth: A Case Study

Aggregate Functions with the HAVING Clause

You learned about the WHERE clause in this chapter when you worked on SELECT statements, which select only certain rows meeting the condition from the original table for later queries. You also learned how to use aggregate functions with the WHERE clause in the previous section. Bear in mind that the WHERE clause will always be applied to the original dataset. This behavior is defined by the SQL SELECT statement syntax, regardless of whether there is a GROUP BY clause or not. Meanwhile, GROUP BY is a two-step process. In the first step, SQL selects rows from the original table or table set to form aggregate groups. In the second step, SQL calculates the aggregate function results. When you apply a WHERE clause, its conditions are applied to the original table or table set, which means it will always be applied in the first step. Sometimes, you are only interested in certain rows in the aggregate function result with certain characteristics...

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SQL for Data Analytics
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