The first project at Metis focused on using Python and Pandas to analyze multiple data sets. We acted as consultants for a fictional non-profit organization, WomenTechWomenYes (WTWY). The organization has a summer gala at the beginning of every summer, and have asked us to help out. WTWY wanted us to use MTA subway data to help optimize the placement of their street teams in order to gather the most signatures, ideally from people who will attend the gala and donate. The proposal:
"WomenTechWomenYes(WTWY) is interested in harnessing the power of data and analytics to optimize the effectiveness of our street team work, which is a significant portion of our fundraising efforts for our annual gala at the beginning of the summer each year.
Where we’d like to solicit your engagement is to use MTA subway data, which as I’m sure you know is available freely from the city, to help us optimize the placement of our street teams, such that we can gather the most signatures, ideally from those who will attend the gala and contribute to our cause."
Project_Benson.ipynb contains the data analysis for this project.
Zip_Zhvi_Summary_AllHomes.csv contains Zillow's Home Value Index data used within this project as well.
The blog post can be found here.