DAT 375 Module 3-1 Assignment
This analysis uses quantitative methods to evaluate scraped data, identifying message and reshare averages per user and peak messaging times. Insights will guide improvements to our social media app's user interface.
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Module 3-1: Choosing The Right Scripts
Robert Wood
Southern New Hampshire University
07/18/2024
Robert Wood
Southern New Hampshire University
07/18/2024
M o d u l e 3-1: Choosing The Right Scripts
After reviewing the problem for this analysis, the most appropriate data analysis technique to
use in order to help with this problem would be a quantitative analysis. The purpose of the analysis that
we are going to run is that we have been asked by our supervisor to use the provided scraped data set
to find the average number of messages by a single username, the average number of reshares by a
single username and finally the time frame that has the highest number of original messages. The data
that we collect from this analysis will help our company to better understand how the competition has
created the user interface for messaging on their social media applications to improve upon and build
upon that for our own social media application. A quantitative approach to this problem is the most
logical fit since we are looking for numerical data from the present and are not looking to make future
predictions at this time.
The scripts that are necessary and that were used to find the average number of messages by a
single username and the average number of reshanes by a single username were pretty similar with only
some slight changes in what was being pulled from the data and from where it was being pulled from.
Meaning that the script essentially pul ed the same thing but from different columns/categories
(messages and reshares). After running the script for the average number of messages by a single
username using the following script we get the output that the average number of messages by a single
username is roughly 945 messages.
SELECT AVG(count)
FROM (
SELECT username,count( - ) as count
FROM assignment3_l.rwDod_table
GROUP BY username
ORDER BY count DESC
After reviewing the problem for this analysis, the most appropriate data analysis technique to
use in order to help with this problem would be a quantitative analysis. The purpose of the analysis that
we are going to run is that we have been asked by our supervisor to use the provided scraped data set
to find the average number of messages by a single username, the average number of reshares by a
single username and finally the time frame that has the highest number of original messages. The data
that we collect from this analysis will help our company to better understand how the competition has
created the user interface for messaging on their social media applications to improve upon and build
upon that for our own social media application. A quantitative approach to this problem is the most
logical fit since we are looking for numerical data from the present and are not looking to make future
predictions at this time.
The scripts that are necessary and that were used to find the average number of messages by a
single username and the average number of reshanes by a single username were pretty similar with only
some slight changes in what was being pulled from the data and from where it was being pulled from.
Meaning that the script essentially pul ed the same thing but from different columns/categories
(messages and reshares). After running the script for the average number of messages by a single
username using the following script we get the output that the average number of messages by a single
username is roughly 945 messages.
SELECT AVG(count)
FROM (
SELECT username,count( - ) as count
FROM assignment3_l.rwDod_table
GROUP BY username
ORDER BY count DESC
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