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SQL for Data Analytics

You're reading from   SQL for Data Analytics Perform fast and efficient data analysis with the power of SQL

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Length 386 pages
Edition 1st Edition
Languages
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Authors (3):
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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
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: Classifying a New Dataset

Solution

  1. The unit of observation is a car purchase.
  2. Date and Sales Amount are quantitative, while Make is qualitative.
  3. While there could be many ways to convert Make into quantitative 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 2: Exploring Dealership Sales Data

Solution

  1. Open Microsoft Excel to a blank workbook.
  2. Go to the Data tab and click on From Text.
  3. Find the path to the dealerships.csv file and click on OK.
  4. Choose the Delimited option in the Text Import Wizard dialog box, and make sure to start the import at row 1. Now, click on Next.
  5. Select the delimiter for your file. As this file is only one column, it has no delimiters, although CSVs traditionally...
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