Led a successful data extraction project for an e-commerce business, building a robust data model and implementing a SQL database. Utilized Python (Pandas) to generate realistic sample data, incorporating trends and variations over five years. Developed effective SQL queries to create aggregate tables and connected them to a dashboarding tool. Presented key performance indicators (KPIs) such as conversion rate, average order value, cart abandonment rate, customer lifetime value (CLV), and return on advertising spend (ROAS) using a Google Looker Studio dashboard. Produced an insightful inference document, demonstrating the capacity to derive valuable insights for data-driven decision-making.
#Roles & Responsibilities: Analyze business stakeholders' data extraction requirements to understand project goals thoroughly. Design an adaptable data model, incorporating UUID for data integrity and realistic sample names. Implement and manage an SQL database to ensure seamless data storage based on the data model. Use Python (Pandas, Faker) to generate a representative sample dataset with five-year trends for analysis. Create efficient SQL queries and aggregate tables to calculate key performance indicators (KPIs). Prepare an insightful inference document presenting data insights and observations. Connect aggregate tables and KPIs to a dashboarding tool for real-time data access and performance tracking..
#Technologies: MySQL, Python(Pandas,Faker), Google Looker Studio, Google SpreadSheet