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

SQL and Analytics

Throughout this chapter, you may have noticed the terms SQL table and dataset are interchangeable. More specifically, it should be clear that SQL tables can be thought of as datasets, rows can be considered as individual units of observation, and columns can be considered as features. If you view SQL tables in this way, you can see that SQL is a natural way to store datasets on a computer.

However, SQL can go further than just providing a convenient way to store datasets. Modern SQL implementations also provide tools for processing and analyzing data through various functions. Using SQL, you can clean data, transform data into more useful formats, and analyze a variety of statistical measures to discover interesting patterns. The rest of this book will be dedicated to understanding how SQL can be used for these purposes productively and efficiently.