Note: Please refer to the files in this repository to find all of the data/code/graphs/tables found in this report and much more.

Tableau Visualizations: Click here

Covid data exploration involves analyzing vast datasets related to the COVID-19 pandemic to gain valuable insights into its spread and impact. Researchers and analysts use various statistical and data visualization techniques to understand trends, patterns, and hotspots. This exploration aids in tracking infection rates, mortality, vaccination progress, and identifying vulnerable populations. By uncovering hidden relationships within the data, decision-makers can make informed strategies to effectively combat the virus and allocate resources where needed most.

Using SQL to analyze COVID-19 data, I extracted and queried relevant information from databases. By writing SQL queries, I gained insights into infection rates, recovery rates, and mortality rates for specific regions and time periods. The power of SQL allowed me to aggregate and filter data effortlessly, facilitating in-depth exploration of the pandemic's impact on various demographics. This data-driven approach aided in identifying trends and informing evidence-based decisions to combat the virus effectively.

Embarking on my first SQL project, I am excited to apply my newfound skills to explore and analyze data in a structured and efficient manner. It's a great opportunity to gain hands-on experience and grow as a data analyst.Leveraging Tableau for my COVID-19 project, I effectively translated data generated from SQL into interactive visualizations, enabling a user-friendly exploration of pandemic trends and their impact. Tableau's intuitive features empowered me to present compelling insights, making data-driven decision-making more accessible and impactful.

The first step involves downloading the dataset. To download the dataset click on this link. The next step being cleaning and dividing the data into Covid Deaths and Covid Vaccinations Workbooks(also provided in the repository) for Data Exploration using SQL. Data Exploration Involves the following steps :

1. Observing the Data.
2. Cleaning the Data.
3. Looking at Global Numbers, Total Cases vs Total Deaths, Total Cases vs Population, Countries with Highest Infection Rate compared to Population, Countries with Highest Death Count per Population. Showing contintents with the highest death count per population and Total Population vs Vaccinations using CTEs and Temp Tables.

Post Exploration it is reqired to prepare the data for Tableau Visualizations by storing the data produced by SQL in Excel Workbooks(provided in the repository). Import the data into Tableau and you are ready to create Visualizations(The Visualizations made by me is attached in the repositiory) .

In conclusion, COVID-19 data exploration in 2022 has been a vital tool in understanding the evolving nature of the pandemic. The analysis has helped identify significant trends, risk factors, and geographical hotspots, guiding public health interventions. Vaccination progress and its impact on infection rates have been key focal points, providing valuable insights for immunization strategies. However, ongoing vigilance is necessary as new variants emerge, emphasizing the continued importance of data-driven decision-making to combat the pandemic effectively in the coming years.