Project Overview
In my project titled "Covid Data Exploration" that utilizes real-time COVID-19 data, I worked with several tables and data manipulations to analyze the impact of the pandemic in various ways. Here's a detailed report on the tables I formed and how I used them in my analysis: Tables Used: CovidDeaths: This table contains data on COVID-19 related deaths worldwide. It includes key fields such as location, date, total_cases, new_cases, total_deaths, and population. I used this table to examine trends in deaths, infection rates, and other metrics across different regions. CovidVaccinations: This table holds data on COVID-19 vaccinations worldwide. Important fields in this table include location, date, and new_vaccinations. I utilized this table to explore vaccination efforts and coverage in various locations. Data Analysis and Manipulations: Data Filtering: I filtered the data from the CovidDeaths table to focus on records where the continent field is not null. The data was sorted by location and date to streamline the analysis process. Total Cases vs. Total Deaths: I calculated the percentage of deaths compared to total cases in the United States to gain insight into the mortality rate. Total Cases vs. Population: Analyzed the percentage of the population infected with COVID-19 across different locations to understand the spread of the virus. Highest Infection Rate and Highest Percentage Population Infected: Grouped data by location to calculate the highest infection counts and percentage of the population infected. This helps identify areas with high infection rates and informs public health measures. Highest Death Count: Calculated the total death count for different locations and continents to highlight areas with the highest mortality rates. Global Numbers: Computed the sum of new cases, new deaths, and the death percentage globally to provide an overview of the worldwide impact of the pandemic. Percentage of Population Vaccinated: By joining the CovidDeaths and CovidVaccinations tables, I calculated the rolling number of people vaccinated and the percentage of the population that has received at least one vaccine dose. This helps track the progress of vaccination efforts. Creating Temporary Tables: I used temporary tables to calculate the percentage of the population vaccinated, including rolling people vaccinated, allowing for intermediate calculations and efficient analysis. Creating Views: Created a view named PercentPopulationVaccinated to store data for later visualizations. The view joins data from CovidDeaths and CovidVaccinations and calculates the rolling people vaccinated. Conclusion: My data exploration project involved using SQL queries to analyze real-time COVID-19 data from multiple perspectives. By filtering, grouping, and calculating various metrics, I gained insights into infection rates, death counts, and vaccination progress. These insights were visualized using Tableau for a comprehensive analysis of the pandemic's impact. The project showcases my abilities in data manipulation, analysis, and visualization.
In the "Covid Data Exploration" project, I analyzed real-time COVID-19 data using SQL to explore trends in cases, deaths, and vaccinations worldwide. By filtering and grouping data, I identified key metrics such as mortality rates, infection rates, and vaccination progress. The project showcases my skills in data manipulation, analysis, and visualization using Tableau.