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 chapters, you learned the basics of data analysis and SQL. You learned how to use CREATE, INSERT, SELECT, ALTER, UPDATE, DELETE, and DROP SQL statements to apply create, read, update, and delete (CRUD) operations on a table. These techniques are the foundation for data analytics.

However, in the real world, as a data analyst, you usually do not handle the entire CRUD flow. To be more specific, you usually do not create datasets from scratch. You will receive data from outside sources. This data is usually in a form that would not fit your needs perfectly and you would need to perform some transform operations to make the data usable. One such operation is the creation of clean datasets from existing raw datasets. The raw data may be missing some information, contain information that is not in the format that fits your needs, or contains information that may not be accurate.

According to Forbes, it is estimated that almost 80% of the time spent...