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
9. Using SQL to Uncover the Truth: A Case Study

Introduction

You have learned simple functions such as CASE WHEN, COALESCE, and NULLIF in Chapter 3, SQL for Data Preparation. These functions receive data from a single row and produce a result for this row. The result of these functions is only determined by the data value in the row and has nothing to do with the dataset it is in. You have also learned aggregate functions such as SUM, AVG, and COUNT in Chapter 4, Aggregate Functions for Data Analysis. These functions receive data from a dataset of multiple rows and produce a result for this dataset. Both types of functions are useful in different scenarios. For example, if you have the physical checkup results of all newborn babies in a country, such as weight and height, you can check each baby's health by checking these measurements to be within a given range using CASE WHEN function. You can also use aggregate functions to get the average and standard deviation of the weight and height of babies in this country. Both types...