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
9. Using SQL to Uncover the Truth – a Case Study

3. SQL for Data Preparation

Activity 5: Building a Sales Model Using SQL Techniques

Solution

  1. Open your favorite SQL client and connect to the sqlda database.
  2. Follow the steps mentioned with the scenario and write the query for it. There are many approaches to this query, but one of these approaches could be:
    SELECT 
    c.*,
    p.*,
    COALESCE(s.dealership_id, -1),
    CASE WHEN p.base_msrp - s.sales_amount >500 THEN 1 ELSE 0 END AS high_savings 
    FROM sales s
    INNER JOIN customers c ON c.customer_id=s.customer_id
    INNER JOIN products p ON p.product_id=s.product_id
    LEFT JOIN dealerships d ON s.dealership_id = d.dealership_id;
  3. The following is the output of the preceding code:
Figure 3.21: Building a sales model query

Thus, have the data to build a new model that will help the data science team to predict which customers are the best prospects for remarketing from the output generated.