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 this book, you have learned a lot about SQL's processing power over numbers and strings. The majority of data analytics tasks are indeed analyzing numbers and strings. However, in the real world, data is often found in various other formats, such as words, locations, dates, and, sometimes, complex data structures. This data, although presented as numbers and strings, has its own domain of operation and computation instead of simple arithmetic. For example, adding one day to January 31, 2022, will result in February 1, 2022, not January 32, 2022.

In this chapter, you will look at these data types and examine how you can use this data in your analysis:

  • Date and time
  • Geospatial
  • JSON
  • Text

By the end of the chapter, you will have broadened your analysis capabilities so that you can leverage just about any type of data available to you.