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

Performing Geospatial Analysis in PostgreSQL

In addition to looking at time series data to better understand trends, you can also use geospatial information (such as city, country, or latitude and longitude) to better understand your customers. For example, governments use geospatial analysis to better understand regional economic differences, while a ride-sharing platform might use geospatial data to find the closest driver for a customer.

You can represent a geospatial location using latitude and longitude coordinates, and this will be the fundamental building block for you to begin geospatial analysis.

Latitude and Longitude

Locations are often thought about in terms of the address—the city, state, country, or postal code that is assigned to the location that you are interested in. This is usually from an analytics perspective. For example, you can look at the sales volume in the ZoomZoom sales table by city and come up with meaningful results about which cities...