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 the previous chapter, you learned how to use SQL to prepare datasets for analysis. Eventually, the purpose of data preparation is to make the data suitable for analysis so that you can make sense of it. Once the data has been prepared, the next step is to analyze it. Generally, data scientists and analytics professionals will try to understand the data by summarizing it and trying to find high-level patterns. SQL can help with this task primarily by using aggregate functions. These functions take multiple rows as input and return new information based on those input rows. To begin, you will learn about aggregate functions.

In this chapter, you will understand the fundamentals of aggregate functions through the following topics:

  • Aggregate Functions
  • Aggregate Functions with the GROUP BY Clause
  • Aggregate Functions with the HAVING Clause
  • Using Aggregates to Clean Data and Examine Data Quality