Prospective Data Scientist

Technical Skills: Python, SQL, C# (.NET Console Applications), Microsoft Excel, R

Education

B.S. Management Information Systems; Minor in Business Analytics | The University of Delaware (May 2026)

Relevant Work Experience

Sports Science & Analytics Intern @ University of Delaware Athletics (October 2023 - April 2024)

• Collected, cleaned, and analyzed sports statistics for the Division 1 football team, evaluating performance trends and forecasting outcomes against conference opponents and higher-ranked teams.
• Generated weekly reports using PivotTables and descriptive statistics to analyze practice data, tracking trends, advancements, and areas for improvement over a 4- to 6-month period.

Research Assistant @ University of Delaware Department of Business Administration (May 2025 - July 2025)

• Conducted exploratory research of 150+ university technology transfer offices and managed data entry on staff backgrounds using Microsoft Excel.

Projects

The projects showcased in this portfolio include work completed both in academic settings and independently. These initiatives demonstrate a strong foundation in data analysis, visualization, and problem-solving, leveraging tools such as Python, SQL, Excel, R/Rstudio, and C#. Each project reflects my ability to collect, clean, and analyze data to derive meaningful insights, whether through coursework, internships, or personal initiatives.

1. College Football Performance Analysis (2021-2023)

This spreadsheet study evaluates college football team performance data from 2021 to 2023 to uncover trends, strengths, and areas for improvement. Using a dataset of team statistics (e.g., points per game, third-down conversions, passing/rushing yards, sacks, and turnovers), I developed multiple tools to provide actionable insights for coaches, analysts, or enthusiasts: PivotTables displaying graphs and trends over the years, a performance heatmap, and a dynamic comparison tool.

This project was one of my first ones utilizing Excel outside of school, and was a developing interest at the time of work. With news of the University of Delaware moving up in the Conference for all of its Division 1 teams, I was tasked with creating charts and tools that would help compare our football teams with other schools to determine our future performance.

[View Project Website Here –>] (https://andrewgarcia08.github.io/college-football_performance-analysis/)

2. Neural Networks Project using Titanic.csv Dataset (2024)

This project demonstrates the application of neural networks to predict passenger survival outcomes based on the Titanic dataset. The work involves tasks such as data loading, preprocessing, model training, and performance evaluation. Key steps include handling missing values, splitting data into training and test sets, and experimenting with various batch sizes and epoch counts to optimize model accuracy. Visualizations of training and validation accuracy, as well as loss plots, are provided to illustrate the model’s learning behavior and optimization process.

While undertaking this neural network experiment, I developed a deeper interest in learning more about this new area of data analytics, particularly how machine learning models can be optimized for real-world applications, from data preprocessing techniques to fine-tuning neural network architectures for better accuracy and efficiency.

[View Project Website Here –>] (https://andrewgarcia08.github.io/NeuralNetworksProject-TitanicDataset/)

3. Capital One SmartBudget AI Tool (2025)

This collaborative experience, working alongside a group of college students throughout the United States, showcases the design of SmartBudget, an AI-powered budgeting tool developed for the 2025 Capital One Case Competition to help young adults manage gig income, student debt, and rising living costs. Key tasks included conducting a medium-sized participant survey, analyzing user financial habits, and translating insights into features such as real-time overspending alerts, gamified rewards, and bite-sized financial education. The work involved strategic rollout planning, competitive analysis against existing tools, and financial modeling. The project earned Semi-Finalist recognition, placing in the top 5% of participating teams.

While developing SmartBudget, I gained a deeper interest in how AI can be applied to real-world financial challenges, particularly in creating personalized, data-driven tools that improve user engagement and financial literacy. This experience strengthened my skills in user research, strategic rollout planning, and translating survey insights into impactful product features.

[View Project Website Here –>] ([https://github.com/andrewgarcia08/CapitalOne-SmartBudget-CaseCompetition/])

4. E-commerce Dataset Project with Python and Flask

This project analyzes an e-commerce sales dataset to uncover category performance, pricing impacts, and seasonal purchasing trends. Using Python in Jupyter Notebook, I cleaned and explored transaction data (including User ID, Product ID, Category, Price, Discount, Final Price, Payment Method, and Purchase Date) to create summary statistics, visualizations, and trend analyses. I then integrated Flask, a lightweight, Python-based web framework, to make it easy to build and deploy a web application for plots and key statistics to be visualized.

This is one of my first projects utilizing the Flask framework under VS Code. The website application is only the beginning of my exploration into web frameworks. This project helped me understand how to connect backend data analysis in Python with a user-friendly web interface. It also sparked my interest in creating more interactive, data-driven applications that combine analytics with real-time user interaction.

[View Project Website Here –>] ([https://github.com/andrewgarcia08/EcommerceDatasetProject-with-Python-and-Flask])

5. TrueSight App: Smart Vision Health Companion

This project involved designing the user interface (UI) and user experience (UX) for the TrueSight smart glasses companion app, focused on continuous vision health monitoring and proactive eye-care management. The core objective was to create an intuitive mobile interface that could bridge the gap between AI-driven health data and the user’s relationship with their eye doctor. Key features designed include the Appointment Sync module for remote data sharing and checkup scheduling, a dynamic Health Tracking dashboard for real-time vision metrics (like Eye Strain and Blink Rate), and a detailed, multi-step User Onboarding flow for secure prescription and health data input. The design also incorporated gamification elements, such as the integrated shopping feature, allowing users to browse and virtually try on frames.

The design process for TrueSight required a focus on user trust and data transparency, particularly concerning AI-driven insights. This project provided hands-on experience in structuring a complex design system to support a two-tier product model (hardware + subscription). My learning was concentrated on translating complex health technology into digestible visual interfaces, ensuring users felt empowered to make informed decisions without being intimidated by data. Ultimately, this project enhanced my ability to design applications that integrate smart hardware data into a seamless, user-controlled health and wellness ecosystem.

[View Project Website Here –>] ([https://github.com/andrewgarcia08/TrueSight_App_Development_UI])