Book Image

SQL for Data Analytics

By : Upom Malik, Matt Goldwasser, Benjamin Johnston
3 (1)
Book Image

SQL for Data Analytics

3 (1)
By: Upom Malik, Matt Goldwasser, Benjamin Johnston

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.
Table of Contents (11 chapters)
9. Using SQL to Uncover the Truth – a Case Study

Statistics with Window Functions

Now that we understand how window functions work, we can start using them to calculate useful statistics, such as ranks, percentiles, and rolling statistics.

In the following table, we have summarized a variety of statistical functions that are useful. It is also important to emphasize again that all aggregate functions can also be used as window functions (AVG, SUM, COUNT, and so on):

Figure 5.10: Statistical window functions

Exercise 17: Rank Order of Hiring

ZoomZoom would like to promote salespeople at their regional dealerships to management and would like to consider tenure in their decision. Write a query that will rank the order of users according to their hire date for each dealership:

  1. Open your favorite SQL client and connect to the sqlda database.
  2. Calculate a rank for every salesperson, with a rank of 1 going to the first hire, 2 to the second hire, and so on, using the RANK() function:
    SELECT *,...