Fair Lending Insights is an end-to-end data engineering pipeline built on the U.S. HMDA mortgage dataset (millions of loan records across 2013–2017). The goal was to ingest raw, multi-file government data, transform it through a medallion architecture on Azure Databricks, and surface business and fair-lending KPIs through SQL analytics including approval-rate gaps across demographic groups, the exact question the dataset was created to answer. Tech Stack Layer Tool Cloud Stor

From Raw Data to a Real Client Recommendation - Here's Everything I Did If you've been looking for a virtual internship that actually teaches you how analysts work in the real world - not just theory - the Quantium Data Analytics Virtual Experience on Forage is one of the best ones out there. I recently completed all three tasks and want to walk you through exactly what I did, what I learned, and what the outputs looked like. Whether you're about to start it yourself or just

ChainForge is an end-to-end data engineering and analytics pipeline built on the DataCo Supply Chain dataset [180,000 records] across 4 years. The goal was to ingest raw data in multiple formats, clean and transform it using cloud tools, and deliver business insights through an interactive Power BI dashboard. Tech Stack Layer Tool Cloud Storage Azure Data Lake Gen2 Pipeline Azure Data Factory Transformation ADF Data Flow (Apache Spark) Visualization Power BI Validation MySQL

For this project, I worked on a credit risk analytics pipeline using Lending Club loan data. The goal was to move beyond simple loan reporting and build a workflow that can identify risky loans early, classify portfolio risk clearly, and support business-style monitoring. This project combines Python for large-file processing and MySQL for risk logic, signal engineering, and portfolio-level analysis. I wanted this project to feel close to how a real financial analytics or ris

For this project, I worked with the official IMDb public datasets and used both Python and SQL to clean, explore, and analyze large-scale entertainment data. The main goal of this project was to understand how real-world raw data can be transformed into a structured and reliable dataset, and then used to answer meaningful business and content-related questions. This project started with data cleaning in Python using pandas, and then moved into analytical queries in both Pytho

Danny’s Dinner | Dataset Danny’s Dinner is a restaurant dataset focused on customer orders, menu items, and membership data. In this project, I analyzed how much each customer spent, how often they visited, which items they ordered first, and which menu items were the most popular. I also worked on customer membership behavior. I explored which items customers bought before becoming members, which items they bought first after joining, and how their purchase patterns changed

The goal of this project was to analyze the Global Superstore sales dataset to understand how the business is performing across different countries, products, and customer segments. The dashboard helps answer important business questions such as: How much revenue the company generates Which products and categories are most profitable Which countries bring the most customers How shipping methods affect customer behavior Whether delivery delays affect customer satisfaction Wher

This project analyzes STM public transit operations in Montreal using Power BI.The goal was to understand how routes, stops, and trips operate across the city and identify patterns like busy routes, peak travel hours, stop activity, and accessibility coverage. The dashboard helps answer operational questions such as: Which routes run the most trips? Which stops are the busiest? What time of the day has the highest trip activity? How accessible are stops for wheelchair users?