Udacity Data Scientist Nanodegree Program Project "Write a Data Science Blog Post" by Juliano Oliveira | July 12, 2021
There is no installation, just this Jupyter Notebook (Python 3.*) file:
This project originated from the Udacity's Nanodregree Data Science Program, to attend the activity "Write a Data Science Blog Post".
The Coronavirus Disease (COVID-19) pandemic took the world by surprise and, despite the advances we have made in dealing with the virus, we still have a lot to learn. However, since last year, the world has produced countless data that can help us understand what is happening and why.
This study aims to apply Data Science techniques to understand the different mortality rates caused by COVID-19 at a global level, based on the five aspects that affect its lethality: economy, corruption, education, health, and government regime.
The proposal is to explain the following issues:
- Economic: Do richer countries perform better against the virus than emerging ones?
- Corruption: Do the most corrupt countries have more deaths?
- Health: Are countries with the most investments in health coping better with the pandemic?
- Education: Does the quality of education interfere with the death rate?
- Government: Is democracy more prepared to save lives than authoritarian regimes?
Datasets available in this repository:
- "covid.csv" (Coronavirus (COVID-19) Deaths (Our World in Data)
- "gdp.csv" (Gross domestic product — GDP (The World Bank)
- "cpi.csv" (Corruption Perception Index 2020 (Transparency International)
- "edu.csv" (Education Index (Human Development Reports)
- "health.csv" (The 2021 STC Health Index (Hudson's Global Residence Index)
- "demo.csv" (Democracy Index 2020 (The Economist Intelligence Unit)
The notebook file is more technical. I answered the questions following the CRISP-DM Process (1. Understanding Business, 2. Understanding Data, 3. Preparing Data, 4. Modeling Data, 5. Evaluate the Results, and 6. Implement). You can find all analyzes of the data and the conclusion in the post (link below).
By crossing and grouping the data from these five aspects of the countries (economy, education, health, corruption, and democracy), it was possible to analyze the correlations between them and, thus, understand the different mortality rates of COVID-19 in the world.
All the explanation and findings are available in my Medium account https://datacgi.medium.com, at the folowing post: https://datacgi.medium.com/five-aspects-affecting-covid-19-death-rate-9f5758a62ef1).
I developed this study for the “Write a Data Science Blog Post,” my first project for Udacity’s Nanodegree Data Science Program. Thank Udacity for encouraging and guiding me to develop this work, my wife and daughter for giving me the support I need to study full time, and my programming teacher and friend Rodrigo Veloso for sharing his knowledge with me.
Juliano Oliveira