SQL Queries Showcase

In this section, I'll briefly highlight some of the SQL concepts and queries I've practiced from the tutorial. These queries cover a range of functionalities, including both fundamental and advanced SQL topics.

Concepts Explored:

• Basic Retrieval: Demonstrated the ability to fetch specific columns from tables using SELECT.

• Filtering and Sorting: Learned how to use WHERE for filtering and ORDER BY for sorting results.

• Joins: Explored joining tables to retrieve information from multiple sources.

• Aggregations: Practiced using aggregate functions like COUNT, AVG, and GROUP BY.

• Advanced Joins: Explored INNER, LEFT OUTER, RIGHT OUTER, and FULL OUTER joins for more complex data retrieval.

• CASE Statements: Utilized CASE statements to apply conditional logic in query results.

• UNION and UNION ALL: Combined and compared data from multiple tables using UNION and UNION ALL.

• Window Functions (PARTITION BY): Introduced window functions to perform calculations within specific partitions of the result set.

These queries showcase my hands-on experience with SQL, ranging from foundational concepts to advanced techniques. While the queries themselves are not included in this document, the associated SQL file provides a comprehensive view of the commands and techniques I've learned.

COVID-19 Data Analysis Project

Purpose:

The purpose of this project is to analyze and derive meaningful insights from the COVID-19 data, exploring key aspects such as total cases, deaths, vaccinations, and their relationships. By leveraging SQL queries and data manipulation techniques, the project aims to provide a deeper understanding of the impact of the pandemic on a global scale, offering valuable insights for informed decision-making.

Queries Overview:

1. Total Cases vs Total Deaths

• Analyzes the relationship between total COVID-19 cases and deaths, calculating the percentage of individuals who succumbed to the virus among those infected.

2. Total Cases vs Population

• Explores the percentage of the population infected with COVID-19 in specific countries, shedding light on the scale of the pandemic's impact.

3. Global Numbers

• Examines global COVID-19 statistics, including new cases, deaths, and the death percentage over time, providing a comprehensive view of the pandemic's progression.

4. Vaccination Analysis

• Investigates the relationship between population, vaccinations, and the cumulative number of vaccinated individuals over time.

Data Handling and Views:

• Addresses data type issues by temporarily converting columns to appropriate types.
• Implements error handling for data conversions.
• Utilizes temporary tables and views for organized data storage.

Conclusion:

In the face of a global health crisis, data-driven insights play a crucial role in understanding the dynamics of the COVID-19 pandemic. This project harnesses the power of SQL to sift through vast datasets, unveiling patterns and trends that contribute to a more comprehensive understanding of the virus's impact on populations worldwide. By analyzing total cases, deaths, and vaccinations, we aim to empower individuals, communities, and decision-makers with valuable information to navigate these challenging times.

SQL Data Cleaning and Enhancement

Overview

This repository includes SQL queries demonstrating data cleaning and enhancement techniques on the Nashville Housing Data database. Below is a summary of the tasks performed:

1. Standardizing Data Format

• Extracting only the date from SaleDate using the CONVERT(Date, SaleDate) function.
• Setting default SaleDate values for better consistency.

2. Populating Missing Data

• Identifying and populating missing PropertyAddress values.
• Using a self-join on ParcelID to intelligently replace NULL values.

3. Address Parsing and Splitting

• Breaking down the PropertyAddress column into separate columns (Address, City).
• Demonstrating two different methods using SUBSTRING and PARSENAME functions.

4. Transforming Yes/No Values

• Transforming 'Y' and 'N' values in the "Sold as Vacant" field to 'Yes' and 'No' using a CASE statement.

5. Removing Duplicates

• Identifying and removing duplicate records with the ROW_NUMBER() function.
• Retaining necessary unique records in the dataset.

6. Deleting Unused Columns

• Streamlining the dataset by dropping unused columns such as OwnerAddress, TaxDistrict, and PropertyAddress.

Conclusion

These SQL operations contribute to the overall cleanliness, consistency, and usability of the data for further analysis.