COVID-19 Data Analysis and Visualisation Using SSMS and Tableau

0. Collecting and Importing the Data

This portfolio project involved analyzing the COVID-19 dataset. Instead of using Excel, I opted to leverage the power of SQL Server Management Studio (SSMS) for data analysis. Here are the reasons behind this decision:

Efficiency: SSMS allows for efficient handling of large datasets, making it suitable for data analysis tasks that Excel may struggle with.

Querying Capabilities: SSMS provides a robust SQL environment that allows for complex data manipulations, filtering, and aggregations.

Database Integration: SSMS seamlessly integrates with SQL databases, facilitating data import, storage, and efficient querying.

The data is downloaded from: https://ourworldindata.org/covid-deaths
To import the data, I transformed the provided CSV file into a suitable format (e.g., Excel) and imported it into SSMS for further analysis.

1. Data Understanding and Exploration

To gain a deeper understanding of the dataset, I first examined the structure and data types of the columns in the CovidData table using the following SQL query in SSMS:

  SELECT 
    COLUMN_NAME, DATA_TYPE
  FROM 
    PortfolioProject.INFORMATION_SCHEMA.COLUMNS
  WHERE 
    TABLE_NAME = 'CovidData';

Furthermore, I checked the percentage of null values in important columns, such as the new_deaths column, to assess the data quality and completeness:

  SELECT 
    (COUNT(*) * 100.0) / (SELECT COUNT(new_deaths) FROM PortfolioProject..CovidData) AS null_percentage
  FROM 
    PortfolioProject..CovidData
  WHERE 
    new_deaths IS NULL;

2. Data Cleaning and Preprocessing

As part of the data cleaning process, I replaced null values in the new_deaths column with 0 using the following SQL update statement:

  UPDATE 
    PortfolioProject..CovidData
  SET 
    new_deaths = 0
  WHERE 
    new_deaths IS NULL;

3. Extracting Tables for Data Visualisation

To extract meaningful insights from the dataset, I constructed SQL queries to obtain specific information for visualisation purposes. Here are a few examples:

Total COVID-19 cases and total deaths: I calculated the total number of COVID-19 cases and total deaths until the present date by using the following SQL query:

  SELECT 
    SUM(new_cases) as total_cases, SUM(cast(new_deaths as int)) as total_deaths, SUM(cast(new_deaths as int))/SUM(New_Cases)*100 as DeathPercentage
  FROM 
    PortfolioProject..CovidData
  WHERE
    continent is not null;

Total COVID-19 deaths by location for continents: I retrieved the total number of COVID-19 deaths by location for each continent using the following SQL query:

  SELECT 
    (COUNT(*) * 100.0) / (SELECT COUNT(new_deaths) FROM PortfolioProject..CovidData) AS null_percentage
  FROM 
    PortfolioProject..CovidData
  WHERE 
    new_deaths IS NULL;

Percentage of population affected by COVID-19: To calculate the percentage of the population affected by COVID-19 for each country, I used the following SQL query:

  SELECT 
    (COUNT(*) * 100.0) / (SELECT COUNT(new_deaths) FROM PortfolioProject..CovidData) AS null_percentage
  FROM 
    PortfolioProject..CovidData
  WHERE 
    new_deaths IS NULL;

Percentage of population affected by COVID-19 over time: To calculate the percentage of the population affected by COVID-19 for each country over time, I used the following SQL query:

  SELECT 
    (COUNT(*) * 100.0) / (SELECT COUNT(new_deaths) FROM PortfolioProject..CovidData) AS null_percentage
  FROM 
    PortfolioProject..CovidData
  WHERE 
    new_deaths IS NULL;

4. Data Visualisation

To create impactful visualisations and better communicate the insights, I loaded the processed data into Tableau. Using Tableau's rich visualisation capabilities, I created various charts, graphs, and interactive dashboards. These visualisations allowed me to effectively present trends, patterns, and comparisons in the COVID-19 data.

By combining data analysis with powerful visualisation tools like Tableau, I was able to derive meaningful insights from the COVID-19 dataset and effectively communicate them to a broader audience. The dashboard can be accessed on my Tableau Public Profile via this link.

5. Conclusion

This portfolio project showcased my skills in data analysis, data cleaning, preprocessing, and data visualisation. By utilizing SQL Server Management Studio for data processing and Tableau for visualisation, I was able to gain insights into the COVID-19 dataset and present them in an engaging and informative manner. Through this project, I demonstrated my ability to extract actionable insights from complex datasets and effectively communicate them using data visualisation techniques.