Shane Kasmarogi | Data Analyst

About me

I'm a Master's candidate in Business Analytics at Loyola University Chicago with experience analyzing complex datasets and building practical data solutions. My work focuses on transforming messy or fragmented data into structured systems that support reporting, forecasting, and decision-making.

I enjoy projects that connect technical analysis with real-world business problems. My experience includes designing relational database systems, building forecasting models in Python, and developing analytical tools that help organizations better understand performance, demand patterns, and operational trends.

My background in psychology shaped how I approach analytics—asking thoughtful questions, identifying patterns in behavior, and translating complex information into insights people can actually use. I'm currently seeking entry-level data, business, or operations analyst roles where I can apply analytics to practical business challenges.

Resume

Projects

Stock Price Forecasting

Developed a Python-based time series forecasting workflow using historical equity data to model market trends and generate a 60-day forward forecast. The analysis includes volatility evaluation and technical indicators to highlight trend shifts, uncertainty intervals, and periods of elevated risk. This project demonstrates forecasting, analytical reporting, and data visualization for decision-focused insights.

Source Code

Closing Price Trend

Daily closing price with moving averages to show long-term and short-term momentum.

Bollinger Bands Analysis

Volatility visualization using dynamic upper and lower bands around the moving average.

60-Day Forecast

Forecast output with trend and uncertainty intervals to support scenario-based analysis.

Chicago Free-Food Network Database

Designed and implemented a relational database system to support tracking of donations, inventory distribution, and recipient activity for a simulated community food resource network. The system centralizes donor, inventory, and distribution data using normalized tables, custom entry forms, automated SQL queries, and formatted reports to improve tracking and operational reporting.

Full Documentation

Relational Schema Overview

This diagram shows the structure of the CFFN database, including donors, recipients, food items, and distribution events.

Navigation Form Interface

The main menu form provides quick access to donor, food item, recipient, and community resource entry forms.

Query Results: Donations by Donor

This query summarizes food contributions by donor name, type, category, and total quantity donated.

Report Output: Donations Summary

A formatted report showing individual donations and summary statistics, used for reporting and community documentation.

Netflix Recommendation System

Developed a content-based recommendation system using TF-IDF vectorization and cosine similarity to identify similar Netflix titles based on textual metadata. The application is deployed in Streamlit and demonstrates practical machine learning applications in personalization, content discovery, and user engagement.

See Live Source Code

Hi, my name is Shane Kasmarogi