In the following SQL queries, I conducted analyses on the COVID-19 dataset to extract insights at both country and continent levels. These queries provide valuable information regarding the overall COVID-19 situation based on total cases, deaths, population, and percentage of population infected.

Country-Based Overall Data: This query aggregates COVID-19 data at the country level, providing insights into the total population, confirmed cases, total deaths, and percentage of population infected. It filters out null continent values to ensure data accuracy.

Continent-Based Overall Data: This query aggregates COVID-19 data at the continent level, showcasing the population, total cases, and total deaths for each continent. It excludes null continent values to ensure consistency in the analysis.

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These SQL queries delve into various aspects of the COVID-19 dataset, exploring the relationship between total cases, total deaths, population, and infection rates across different countries. By filtering data country-wise, these queries offer insights into the impact of the pandemic on individual nations.

Total Case vs Total Death:

• This query compares the total number of COVID-19 cases with the total number of deaths recorded over time.

• It calculates the death percentage to understand the severity of the pandemic in each location.

• The coalesce() function ensures that null values are replaced with 0 for better visualization.

Population vs Total Cases:

• This query examines the relationship between a country's population and the total number of COVID-19 cases.

• It calculates the infection rate, representing the percentage of the population infected by the virus.

• The results provide insights into the scale of the outbreak relative to the population size.

Population vs Highest Total Cases:

• This query identifies the country with the highest recorded total cases of COVID-19.

• It compares the population of each country with its highest infection count and calculates the corresponding infection rate.

The results highlight countries with the most significant impact of the pandemic relative to their population size, aiding in understanding the spread and severity of COVID-19 outbreaks globally.

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In the following SQL queries, I've analyzed the COVID-19 dataset to identify countries and continents with the highest death counts, as well as to explore global case-death ratios. These insights are crucial for understanding the severity of the pandemic's impact at both country and continent levels.

Countries with Highest Death Count:

• This query provides a list of countries with the highest death counts due to COVID-19.

• It filters out null continent values to ensure data accuracy.

• Results are grouped by country and ordered based on the highest death count, revealing the most severely affected countries.

Continent-Based Overall Data: 

• This query determines the continent with the highest death count attributed to COVID-19.

• It filters out null continent values to ensure data integrity.

• Results are grouped by continent and ordered by the highest death count, providing insights into the continents most impacted by the pandemic.

Global Case-Death Data with Ratio:

• This query computes the case-death ratio for each continent, providing insights into the severity of COVID-19.

• It includes total cases, total deaths, and the calculated death ratio, rounded for clarity.

• Results are grouped by continent and ordered by the total death count, offering a comprehensive overview of the pandemic's impact on a global scale.

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The following SQL queries analyze the global COVID-19 dataset to provide insights into the total number of new cases and new deaths. By examining these metrics both cumulatively and by date, we gain a comprehensive understanding of the pandemic's progression and its impact over time.

Global Total New Cases & New Deaths by Date:

• This query aggregates data to calculate the total number of new cases and new deaths globally for each date.

• The coalesce() function ensures that null values are replaced with 0 for visualization clarity.

• It also computes the death percentage, representing the proportion of new deaths relative to new cases on each date.

Global Total New Cases & New Deaths:

• This query provides the cumulative total number of new cases and new deaths globally.

• It computes the death percentage, representing the proportion of new deaths relative to new cases globally.

• The results offer a comprehensive overview of the pandemic's impact, aiding in understanding its severity and progression.

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The following SQL queries focus on joining two tables, Case_data and Vacci_data, to analyze the relationship between total population and vaccination data. By examining this relationship, we gain insights into vaccination coverage and its progression over time, essential for understanding the pandemic's trajectory and response efforts.

Joining Tables Case_data and Vacci_data:

• This query combines data from the Case_data and Vacci_data tables to compare total population with vaccination statistics.

• The coalesce() function ensures that null values are replaced with 0 for visualization clarity.

• Results are ordered by location and date to facilitate analysis and interpretation.

Using Common Table Expression (CTE) for Vaccination Percentage:

• This query employs a Common Table Expression (CTE) named popVSvac to calculate vaccination percentages by date.

• The coalesce() function ensures that null values are replaced with 0 for visualization clarity.

• The percentage of population vaccinated is computed based on the rolling vaccination population divided by the total population, providing insights into vaccination coverage over time.

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