Tools Used
- SQL: Subqueries, Joins, Aggregations, Views
- Entity-Relationship (ER) Modeling
Dataset Focus Customer activity data across checking and credit card products.
Project Overview Developed a dynamic KPI reporting system to evaluate customer engagement across multiple banking products. Leveraged normalized datasets to derive key performance indicators, including:
- Customer Tenure
- Total Active Accounts
- Aggregate Assets
- Product Join Dates
Key Features & Highlights
- Customer Relationship Start Date Identified the earliest interaction date across multiple product tables for accurate tenure calculation.
- Account Activity Aggregation Calculated total number of active accounts per customer, spanning checking and credit card products.
- Asset Summation Computed total customer assets across account types, including support for both positive and negative balances.
- Optimized Query Design Crafted efficient SQL queries using JOINs and aggregations tailored for financial data analysis and real-world reporting.
Tools Used SQL (CTEs, Aggregates, UNION, Joins)
Database Focus Product sales and POS transaction data across multiple tables
Overview Analyzed multi-source product sales data by merging and cleaning records. Generated insights such as total and average sales per product, duplicates identification, and transaction-level aggregation across POS systems.
Key Features Identified and cleaned duplicate entries between product datasets. Created merged views using UNION and cleaned datasets using DISTINCT. Generated consolidated sales summaries across three POS tables. Computed KPIs including total sales, number of transactions, and averages per product.
Tools Used SQL (Window Functions, Joins, Group By, Case Statements)
Database Focus Transaction log and customer account data
Overview Built queries to classify, summarize, and analyze customer transaction behavior. Derived credit/debit stats, recent account activity, and customer summaries by combining transactional and account-level data.
Key Features Extracted latest accounts per customer using MAX(create_date). Classified transaction types as credit or debit with CASE statements. Joined account and transaction tables to produce unified customer reports. Handled customers with missing account records using LEFT JOIN logic.
Tools Used SQL (DDL, DML, Query Logic, Relational Joins)
Database Focus Library management schema design and advanced querying
Overview Designed and implemented a relational database schema for a library system. Populated tables with sample data and wrote complex SQL queries to simulate real-world operations such as loan tracking, book availability, and borrower behavior.
Key Features Designed schema with normalized tables (books, authors, branches, borrowers, loans). Populated database using INSERT statements with realistic values. Executed complex queries including: Borrowers by branch Average copies and loaned books Books borrowed by author or city Ranking of library branches based on loan activity
Performed data manipulation tasks UPDATE branch info, DELETE books by publisher, INSERT new titles.