Skip to content

This is a capstone research project for my Certificate in Applied Data Science (CADS) at my undergraduate institution, Wesleyan University, on the topic of "Understanding the Variances in COVID-19 Pandemic Outcome - Excess Mortality - with Social, Cultural, and Environmental Factors", sponsored by Prof. Maryam Gooyabadi.

Notifications You must be signed in to change notification settings

Cyanjiner/CADS-Capstone

Repository files navigation

Socio-economic, cultural, and environmental influences on COVID-19 pandemic outcome -- excess mortality

DESCRIPTION:

This is a capstone research project for the Certificate in Applied Data Science (CADS) at my undergraduate institution, Wesleyan University, on the topic of "Understanding the Variances in COVID-19 Pandemic Outcome - Excess Mortality - with Social, Cultural, and Environmental Factors".

  • AUTHOR: Jiner Zheng
  • FACULTY SPONSOR: Prof. Maryam Gooyabadi

Useful Code Sources:

  • DTW.Rmd contains code for data cleaning, data management, functions of producing excess deaths using ARIMA/GARCH, & implementations of hierarchical clustering analyses on Time Series data based on Euclidean distances and Dynamic Time Warping (DTW) distances.

  • K-medoids.Rmd contains code for implementing K-Medoids clustering on mix-typed data with computations of the Gower distance.

Note: you can also set up the environment using JZ-capstone.Rproj in R.

Other Materials:

About

This is a capstone research project for my Certificate in Applied Data Science (CADS) at my undergraduate institution, Wesleyan University, on the topic of "Understanding the Variances in COVID-19 Pandemic Outcome - Excess Mortality - with Social, Cultural, and Environmental Factors", sponsored by Prof. Maryam Gooyabadi.

Topics

Resources

Stars

Watchers

Forks

Languages