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


SQL provides you with many tools for mixing and cleaning data. In this chapter, you first learned how to combine two or more tables. You started with the JOIN keyword, which fuses data from tables based on their common columns. There are several types of JOIN. Depending on whether you want to retain the data in a certain table or not, you can choose INNER JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN, FULL OUTER JOIN, or CROSS JOIN. You then learned how to use subqueries and CTEs to preserve and reuse the results of queries. You can also use UNION and UNION ALL to merge the results of two queries with the same structure into one result set.

After learning how to combine data from different datasets, you learned how to perform certain transformations on the data. You first started with the CASE WHEN function, which is a generic way to convert one expression into another based on custom-defined conditions. You then learned how to use the COALESCE() and NULLIF() functions to convert...