Covid-19 Data - A Data Science Project

This project is part of the course Applied Data Science with Python

Name: Fabian Heudorfer

Course: Applied Data Science with Python

Project Description:

Based on the Covid-19 dataset from ‘Our World In Data’ https://ourworldindata.org I want to visualize the effects on all countries in the world.

Our World in Data:

https://github.com/owid/covid-19-data/

https://github.com/owid/covid-19-data/blob/master/public/data/README.md

https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv

All Available Colums in the Dataset:

['iso_code', 'continent', 'location', 'date', 'total_cases', 'new_cases', 'new_cases_smoothed', 'total_deaths', 'new_deaths', 'new_deaths_smoothed', 'total_cases_per_million', 'new_cases_per_million', 'new_cases_smoothed_per_million', 'total_deaths_per_million', 'new_deaths_per_million', 'new_deaths_smoothed_per_million', 'reproduction_rate', 'icu_patients', 'icu_patients_per_million', 'hosp_patients', 'hosp_patients_per_million', 'weekly_icu_admissions', 'weekly_icu_admissions_per_million', 'weekly_hosp_admissions', 'weekly_hosp_admissions_per_million', 'new_tests', 'total_tests', 'total_tests_per_thousand', 'new_tests_per_thousand', 'new_tests_smoothed', 'new_tests_smoothed_per_thousand', 'positive_rate', 'tests_per_case', 'tests_units', 'total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'total_boosters', 'new_vaccinations', 'new_vaccinations_smoothed', 'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred', 'total_boosters_per_hundred', 'new_vaccinations_smoothed_per_million', 'new_people_vaccinated_smoothed', 'new_people_vaccinated_smoothed_per_hundred', 'stringency_index', 'population', 'population_density', 'median_age', 'aged_65_older', 'aged_70_older', 'gdp_per_capita', 'extreme_poverty', 'cardiovasc_death_rate', 'diabetes_prevalence', 'female_smokers', 'male_smokers', 'handwashing_facilities', 'hospital_beds_per_thousand', 'life_expectancy', 'human_development_index', 'excess_mortality_cumulative_absolute', 'excess_mortality_cumulative', 'excess_mortality', 'excess_mortality_cumulative_per_million']

Preselected Columns to Reduce Complexity:

['total_cases_per_million', 'new_cases_smoothed_per_million', 'total_deaths_per_million', 'total_vaccinations_per_hundred', 'people_fully_vaccinated_per_hundred','icu_patients_per_million', ]

Goal:

Visuallize and compare the Covid-19 effects on the countries worldwide. The results should be presented as a python dashboard.

How To Use The Code:

1. Clone the gitHub repository:
   >> git clone https://github.com/FabianHeu/DataScienceProject.git
2. Run the python script main.py
   >> python main.py
3. Check out the dash server on your browser http://127.0.0.1:8050/

Sources:

The App Design is based on:

https://dash.gallery/dash-uber-rides-demo/

##Jupyter Notebook:

To test single functions and play around with the code there is jupyter notbook called main.ipynb in the jupyter subfolder.
>> jupyter notebook main.ipynb