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


You have just completed your first real-world data analysis problem using SQL. In this chapter, you developed the skills necessary to develop hypotheses for problems and systematically gather the data required to support or reject them. You started this case study with a reasonably difficult problem of explaining an observed discrepancy in sales data and discovered two possible sources (launch timing and marketing campaign) for the difference while rejecting one alternative explanation (sales price).

While being a required skill for any data analyst, being able to understand and apply the scientific method in your exploration of problems will allow you to be more effective and find interesting threads of investigation. In this chapter, you used the SQL skills you have developed throughout this book, from simple SELECT statements to aggregating complex data types, as well as windowing methods. After completing this chapter, you will be able to continue and repeat this type...