Inspiration
My teammate and I are both part of clubs that are not extremely diverse, and wanted to see where we were going wrong + figure out how to best fix it. We decided to take a look at our club's recruitment data and identify trends.
What it does
This python script returns standard statistics about each stage in our recruitment process.
How we built it
We wrote this in python with the pandas and numpy number manipulation libraries! We ran with jupyter notebook.
Challenges we ran into
Our data was not the cleanest. We either had to go in and manually correct errors in the input data or have our queries account for that in some way.
Accomplishments that we're proud of
We came up with a lot of concrete insights for our club; and these are leading to action items we are taking going into a new semester!
What we learned
We had a lot of fun playing around with the pandas libraries, and looked into new methods we'd previously never knew about!
What's next for DEI Analytics For Applications
Potentially we'd like to standardize this script, and maybe create a way to normalize all application data into the format the script likes so other clubs can also use!
Installing and Running We ran the Jupyter notebook code in the Berkeley Jupyter Hub.
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