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Overview of this book

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you. SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation. By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
Preface
Free Chapter
1. Understanding and Describing Data
2. The Basics of SQL for Analytics
3. SQL for Data Preparation
4. Aggregate Functions for Data Analysis
5. Window Functions for Data Analysis
6. Importing and Exporting Data
7. Analytics Using Complex Data Types
8. Performant SQL
9. Using SQL to Uncover the Truth – a Case Study

Aggregate Functions with GROUP BY

We have now used aggregate functions to calculate statistics for an entire column. However, often, we are not interested in the aggregate values for a whole table, but for smaller groups in the table. To illustrate, let's go back to the `customers` table. We know the total number of customers is 50,000. But we might want to know how many customers we have in each state. How would we calculate this?

We could determine how many states there are with the following query:

`SELECT DISTINCT state FROM customers;`

Once you have the list of states, you could then run the following query for each state:

`SELECT COUNT(*) FROM customer WHERE state='{state}'`

Although you can do this, it is incredibly tedious and can take an incredibly long time if there are many states. Is there a better way? There is, and it is through the use of the `GROUP BY` clause.

GROUP BY

GROUP BY is a clause that divides the rows of a dataset into multiple...