I thrive on transforming complex data into meaningful insights that drive business decisions. Recently completed my MS in Business Analytics, I combine analytical expertise, programming proficiency, and a passion for data-driven solutions to uncover actionable insights from data.

With a blend of technical skills and a business-oriented mindset, I aim to bridge the gap between raw data and impactful decision-making.

• Programming Languages: Python, R, Rust (exploring)
• Data Visualization: Power BI, Plotly, Seaborn
• Databases: SQL, NoSQL, SSAS tabular models
• ETL/ELT: Airflow, Azure Data Factory, dbt
• Tools: Git, Jupyter, Visual Studio Code

I'm actively expanding my portfolio with projects that challenge and grow my skill set. Here are some current initiatives:

Developed a convolutional neural network (ConvNet) model on the MNIST dataset, achieving a 99.3% accuracy rate. I further explored the model's weights and biases to gain deeper insights into network architecture and performance.

Explore the project on GitHub.

Created a command-line game where players navigate a starship across the galaxy, facing random scenarios generated by ChatGPT (at a higher temperature setting) to create dynamic, unpredictable challenges.

View the source code on Github.

• Conducting an A/B testing project to evaluate product changes.
• Performing sentiment analysis on comments from the Minnesota Vikings subreddit to track fan emotions and trends (SKOL).

Analyzed the Minnesota Vikings subreddit to uncover key themes and narratives shaping the fan community. Utilized LDA topic modeling and sentiment analysis on 4.5 million comments to gauge overall sentiment. Demonstrated a practical application of findings with an OpenAI chatbot that engages with fan discussions dynamically.

See what Vikes fans jabber about.

Developed regression and classification models to analyze beer sales data, predicting sales channels and spend per trip. Utilized Python and R for data processing, visualization, and model building, achieving significant insights into sales patterns and customer behavior.

• Data Cleaning and Preparation: Loaded and preprocessed beer sales data to ensure quality and consistency.
• Exploratory Data Analysis (EDA): Visualized data to uncover patterns and relationships, aiding in feature engineering.
• Model Building: Trained decision tree, support vector classifier, Lasso regression, and neural network models to predict sales outcomes.
• Model Evaluation: Assessed model performance using metrics like accuracy, sensitivity, specificity, precision, RMSE, MAE, and R-squared.
• Feature Engineering: Created new features to enhance model performance, including binary columns for weekend/weekday and categorized spend per trip.

This project showcases my ability to handle real-world data, perform comprehensive analysis, and build predictive models that provide actionable business insights.

• University of Denver – Master of Science in Business Analytics (Graduated August 2024)
• University of Minnesota, Twin Cities – Bachelor of Science in Business Administration (Management Information Systems & Finance) (Graduated May 2017)

• Deep diving into PyTorch, implementing MLPs, ConvNets, and RNNs.
• Experimenting with ChatGPT API and Retrieval-Augmented Generation (RAG) applications.

I’m always open to collaboration, networking, or simply talking shop about data science and analytics. Feel free to reach out!