Sundip Sharma | Data Analysis Portfolio

, , ,

By

sunnyd200

·

COVID Data Analysis Project

Challenges in business are a given, but it’s our response to them that defines our trajectory. Looking beyond the immediate obstacle, there lies a realm of opportunity and learning.

Project #1

An analysis of the COVID cases globally. Looking at the percentage of the population infected per country and the deaths per continent from 2019 to 2023. Using Tableau, added a forecast analysis to predict the average percentage of population infected for the following 2 years in 5 countries, United Kingdom, United States, China, Brazil and India.

COVID Dashboard – click on the image to view an interactive version of the map!
Public dataset from WHO 
This dataset is available publically via the World Health Organisation (https://ourworldindata.org/covid-deaths).

The data analysis was conducted as follows:

1. Initial Data Exploration and Selection

  • Initial View: Selects all columns from the CovidDeaths table, excluding rows where the continent is null (which typically represent aggregated data for continents or income groups, thus focusing on country-level data initially).
  • Data Selection: A specific query selects the core columns for analysis: Location, date, total_cases, new_cases, total_deaths, and population.

2. Country-Level Calculations (Death & Infection Rates)

  • Death Percentage: Calculates the Case Fatality Rate (CFR) (or death likelihood) for a specific country (e.g., those matching location like ‘%states%’).{Death Percentage} = ({total\_deaths} / {total\_cases}) \times 100
  • Percent Population Infected: Calculates the percentage of the population that has contracted COVID-19 for each country over time. {Percent Population Infected} = ({total\_cases} /{population}) \times 100
  • Highest Infection Rate: Finds the maximum infection rate recorded for each country and ranks them in descending order to identify the most infected areas.
  • Highest Death Count: Aggregates the maximum total deaths for each country to rank them by the overall death toll.

3. Continental and Global Aggregation

  • Continental Death Count: Aggregates total deaths by continent (where continent is not null) to compare the death toll between continents.
  • Global Numbers (Daily): Calculates the daily global totals for new cases, new deaths, and the daily global death percentage by summing new_cases and new_deaths across all countries grouped by date.
  • Global Totals (Overall): Calculates the overall total cases, total deaths, and the overall global death percentage across the entire dataset.

4. Joining Data and Vaccination Analysis

Rolling People Vaccinated: A Window Function (SUM... OVER (Partition by ... ORDER BY ...)) is used to calculate the running total of new_vaccinations for each location over time. This creates a new column called RollingPeopleVaccinated.

Joining Tables: The CovidDeaths (dea) and CovidVaccinations (vac) tables are JOINED on location and date.

Tags: