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

Using Aggregates to Clean Data and Examine Data Quality

In Chapter 3, SQL for Data Preparation, you learned how SQL can be used to clean data. While the techniques mentioned in that chapter do an excellent job of cleaning data, aggregates add a number of techniques that can make cleaning data even easier and more comprehensive. In this section, you will look at some of these techniques.

Finding Missing Values with GROUP BY

As mentioned in Chapter 3, SQL for Data Preparation, one of the biggest issues with cleaning data is dealing with missing values. You learned how to find missing values and how to resolve this issue. In this chapter, you will learn how to determine the extent of missing data in a dataset.

Using aggregates, identifying the amount of missing data can tell you not only which columns have missing data but also the usability of the columns when so much of the data is missing. Depending on the extent of missing data, you will have to determine whether it...