Book Image

SQL for Data Analytics - Third Edition

By : Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston
Book Image

SQL for Data Analytics - Third Edition

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)
9. Using SQL to Uncover the Truth: A Case Study


In this chapter, you learned how to calculate the statistical properties of a dataset using aggregate functions, such as the average, count, minimum, maximum, and standard deviation. Aggregate functions themselves are applied to a whole dataset. In order to use them to analyze the statistics of sub-datasets inside a larger dataset, you also learned about the GROUP BY clause of the SELECT statement, which divides a large dataset into smaller ones based on the keys you provided and applies aggregate functions to each of the groups.

To make the GROUP BY clause more useful, several additional properties were introduced, most importantly the HAVING clause. This HAVING clause is used to filter the values of aggregated groups. It is applied at the second stage of the GROUP BY clause execution and should be distinguished from the WHERE clause, which is applied to the original data table or table set and is applied at the first stage of the GROUP BY execution.

Now that you learned...