https://github.com/kavitha-19/sqlcoviddataexplorationproject
https://github.com/kavitha-19/sqlcoviddataexplorationproject
microsoft-sql-server sql
Last synced: 5 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/kavitha-19/sqlcoviddataexplorationproject
- Owner: kavitha-19
- Created: 2024-01-23T01:50:40.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-23T02:40:41.000Z (over 2 years ago)
- Last Synced: 2025-03-12T04:20:50.448Z (over 1 year ago)
- Topics: microsoft-sql-server, sql
- Homepage:
- Size: 20.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# COVID-19 Data Exploration Project
## Overview:
This project involves the exploration and analysis of COVID-19 data using SQL queries. The dataset includes information on COVID-19 cases, deaths, vaccinations, and population statistics across different locations and continents.
## Objectives:
- Understanding the Impact:
- Analyzing the total cases and deaths to understand the severity of the COVID-19 impact in various locations.
- Calculating death percentages to assess the likelihood of fatality in specific regions.
- Population Impact:
- Examining the percentage of the population infected with COVID-19 to gauge the scale of the outbreak.
- Identifying countries with the highest infection rates compared to their populations.
- Vaccination Analysis:
- Investigating the progress of COVID-19 vaccination campaigns.
- Calculating the percentage of the population that has received at least one COVID-19 vaccine.
- Geographical and Temporal Trends:
- Analyzing trends across continents, countries, and over time.
- Identifying regions with the highest death count per population.
## SQL Techniques Used:
- Joins: Combining data from different tables to enrich the analysis.
- Common Table Expressions (CTEs): Utilizing CTEs for clearer and modular queries.
- Window Functions: Employing window functions for calculations over partitions.
- Aggregate Functions: Summarizing and aggregating data for higher-level insights.
- Temporary Tables: Using temporary tables for intermediate data storage.
- Creating Views: Building views to store specific datasets for later use.
## Skills Demonstrated:
- Data Exploration: Extracting valuable insights from complex datasets.
- SQL Proficiency: Leveraging a variety of SQL functionalities for analysis.
- Data Visualization Preparation: Preparing data for later visualization tools.
- Problem Solving: Addressing questions related to COVID-19 impact and vaccination trends.
## Future Steps:
This project lays the groundwork for further analysis and visualization.