Alidzhon Aminov

SQL Analysis of COVID-19 Data

In this project, I analyzed COVID-19 data to understand the pandemic's impact across different countries and continents. Using SQL, I delved into various aspects of the data to extract meaningful insights. The primary steps of my analysis included:

• Assessing Daily Infection Rates: Calculated the daily percentage of the population infected with COVID-19 per country, giving insight into how the virus spread in different countries.
• Identifying High Infection Rates: Showed countries with the highest infection rates by comparing the maximum number of total cases against the population.
• Analyzing Death Counts: By examining the maximum number of total deaths per country, I identified where COVID-19 had been most deadly.
• Comparing Death and Infection Rates: Combined the total cases of infected people with death data to calculate the percentage of deaths among the infected, which showcases the lethality of the virus in different countries and could be a good start for comparing and analyzing countries' healthcare systems.
• Exploring Vaccination Progress: Analyzed the daily and cumulative vaccination numbers per country, by calculating the percentage of the population vaccinated to measure the progress of vaccination against COVID-19.
• Creating Views for Easy Access: For the future analysis, I created views that summarize key metrics like total cases, death percentages, and vaccination rates, making the data more accessible.
• Developing a Stored Procedure: Created a stored procedure to retrieve comprehensive COVID-19 information for any given country, including confirmed cases, death percentage, and vaccination rates, to simplify the process of extracting specific insights.