Assume you are giving a dataset that is based on user surveys. You notice that many surveys have missing fields. You also notice that some surveys have fields on a scale of 1 to 5 while others are on scale of 1 to 10. The survey has mixed information using metric measurements and the imperial system. There are some different fields depending on the country, but they all relate to the location of the participant. You also notice that some values just don't make sense. What analysis would you do on the data set to come up with a data cleaning plan? What are some normalization and cleaning tasks you could do based on the information provided in this description?

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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Assume you are giving a dataset that is based on user surveys. You notice that many surveys have
missing fields. You also notice that some surveys have fields on a scale of 1 to 5 while others are on a
scale of 1 to 10. The survey has mixed information using metric measurements and the imperial
system. There are some different fields depending on the country, but they all relate to the location
of the participant.
You also notice that some values just don't make sense.
What analysis would you do on the data set to come up with a data cleaning plan?
What are some normalization and cleaning tasks you could do based on the information provided in
this description?
Edit View Insert Format Tools Table
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Transcribed Image Text:Assume you are giving a dataset that is based on user surveys. You notice that many surveys have missing fields. You also notice that some surveys have fields on a scale of 1 to 5 while others are on a scale of 1 to 10. The survey has mixed information using metric measurements and the imperial system. There are some different fields depending on the country, but they all relate to the location of the participant. You also notice that some values just don't make sense. What analysis would you do on the data set to come up with a data cleaning plan? What are some normalization and cleaning tasks you could do based on the information provided in this description? Edit View Insert Format Tools Table 12pt v Paragraph BIU Αν 2 T² : PAYTO THE MARCELLA M. SERRANO PEDRO GOMEZ 3610 COUNTRY TRAILS PH. 619 S BONITA, CA 91902 91902 Ale 2512 12759 Eraser ser made in ape g GRIP 12 31 Super Polymer Lead Pentel
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