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

Text Analytics Using PostgreSQL

In addition to performing analytics using complex data structures within PostgreSQL, you can also make use of the non-numeric data available. Often, the text contains valuable insights. For instance, you can imagine a salesperson keeping notes on prospective clients, such as "Very promising interaction, the customer is looking to make a purchase tomorrow," is valuable data, as does this note: "The customer is uninterested. They no longer have a need for the product." While this text can be valuable for someone to manually read, it can also be valuable in the analysis. Keywords in these statements, such as "promising," "purchase," "tomorrow," "uninterested," and "no," can be extracted using the right techniques to try to identify top prospects in an automated fashion.

Any block of text can have keywords that can be extracted to uncover trends or make predictions—for example...