This project analyzes COVID-19 case data using SQL. It includes queries for data exploration, trend analysis, and insights into infection rates, recovery, and mortality. The dataset consists of global COVID-19 cases recorded over time, allowing us to gain valuable insights into the pandemic.
- Data Cleaning: Handling missing values, duplicates, and inconsistencies.
- Exploratory Data Analysis (EDA): Analyzing trends in infections, recoveries, and deaths.
- Aggregations & Grouping: Summarizing data by country, region, and time.
- Joins & Subqueries: Combining multiple tables for deeper insights.
- Visualization Support: Queries designed for easy integration with visualization tools.
- SQL (MySQL / PostgreSQL / SQL Server / SQLite)
- Database Management Systems (DBMS)
- Data Visualization Tools (optional, e.g., Tableau, Power BI, Excel)
- Total Cases Over Time
- Daily New Cases and Deaths
- Country-wise Case Comparison
- Recovery and Fatality Rates
- Top Affected Countries
- Download the dataset (CSV or SQL dump).
- Import it into your SQL database.
- Run the provided SQL queries to analyze the data.
- Open your SQL client (e.g., MySQL Workbench, pgAdmin, SSMS).
- Load the dataset into the database.
- Execute SQL queries to extract insights.
- Optionally, export the results for visualization.
- Automate data updates with API integration.
- Advanced statistical analysis using Python.
- Interactive dashboards with Power BI or Tableau.
Subhankar Ghosh
This project is licensed under the MIT License.