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SQL for Data Analytics

You're reading from   SQL for Data Analytics Harness the power of SQL to extract insights from data

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801812870
Length 540 pages
Edition 3rd Edition
Languages
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Authors (4):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Jun Shan Jun Shan
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Jun Shan
Upom Malik Upom Malik
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Upom Malik
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Toc

Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics FREE CHAPTER 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 Appendix

1. Understanding and Describing Data

Activity 1.01: Classifying a New Dataset

Solution:

  1. The unit of observation is a car sale.
  2. Date and Sales Amount are quantitative, while Make is qualitative.

While there could be many ways to convert Make into numeric data, one commonly accepted method would be to map each of the Make types to a number. For instance, Ford could map to 1, Honda could map to 2, Mazda could map to 3, Toyota could map to 4, Mercedes could map to 5, and Chevy could map to 6.

Activity 1.02: Exploring Dealership Sales Data

Solution:

  1. Open Microsoft Excel to a blank workbook.
  2. Go to the Data tab and click on Get Data | From File | From Text/CSV.
  3. Find the path to the dealerships.csv file and click on Import.
  4. In the file import window, click on Load. The following table is what you will see when the file loads.

Figure 1.38: The dealerships.csv file loaded

A histogram of the results may vary a little...

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