https://github.com/sksubhadeep/covid-19-portfolio-project-using-sql
SQL Portfolio Project on COVID-19 Dataset
https://github.com/sksubhadeep/covid-19-portfolio-project-using-sql
covid-19 cte sql views
Last synced: 3 months ago
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SQL Portfolio Project on COVID-19 Dataset
- Host: GitHub
- URL: https://github.com/sksubhadeep/covid-19-portfolio-project-using-sql
- Owner: sksubhadeep
- Created: 2023-08-16T11:25:42.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-17T03:34:20.000Z (almost 2 years ago)
- Last Synced: 2025-02-20T02:21:45.617Z (3 months ago)
- Topics: covid-19, cte, sql, views
- Homepage: https://ourworldindata.org/covid-deaths
- Size: 8.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
The COVID-19 Data holds critical insights about a global pandemic that has impacted us all. The data encompassing information from 8th January 2020 to 9th August 2023.
The dataset is divided into two subsets: COVID Deaths and COVID Vaccination.
🔍 Here's a sneak peek into what I accomplished:
- Explored and viewed all columns from the COVID Deaths Table.
- Explored and viewed all columns from the COVID Vaccination Table.
- Extracted specific columns like location, date, total_cases, new_cases, total_deaths, and population from the COVID Deaths Table.
- Calculated the percentage of total deaths in relation to total cases for all countries, shedding light on the likelihood of fatality if one contracts COVID-19 in a specific country.
- Analyzed the total cases as a percentage of the population in Canada.
🌍 The Insights Grew Deeper:
- Identified countries with the highest infection rates compared to their population.
- Discovered the countries with the highest death count per population.
- Analyzed continents with the highest death count per population, painting a grim picture of the global impact.
📈 Tracking the Numbers:
- Monitored the global count of new COVID-19 cases and new deaths caused by the virus.
💉 Vaccination Analysis:
- Explored the relationship between total population and vaccinations by joining the two tables based on location and date.
- Employed Common Table Expressions (CTEs) to delve deeper into the population-vaccination dynamics.
- Utilized temporary tables to further understand the correlation between population and vaccinations.
- Created views to present a clearer picture of the population-vaccination connection.