Technical Skills
Languages, frameworks, and tools I use to build data science solutions — with proficiency levels and links to projects where I've applied them.
Languages
Core programming languages for data science and software development
Used in: Rotten Tomatoes Predictor, Kaggle Challenges
Used in: Prop Trading Dashboard, ETL pipelines
Used in: Prop Trading Dashboard
Used in: CU Boulder MSDS coursework
Machine Learning & Deep Learning
Frameworks and techniques for building predictive models
Used in: Kaggle Challenges (GANs, CNNs, RNNs)
Used in: Rotten Tomatoes Predictor
Used in: Classification, regression, and clustering across multiple projects
Used in: BERT fine-tuning, NLP pipelines
BERT, BiGRU, BiLSTM, tokenization, embeddings, text classification
CNNs, GANs, style transfer, medical image classification
Statistics & Analysis
Statistical methods and analytical techniques
t-tests, chi-squared, A/B testing, p-value interpretation
Linear, logistic, ridge, lasso, polynomial regression
One-way, two-way, repeated measures ANOVA
Core data manipulation stack used across all Python projects
Data Engineering
Databases, pipelines, and infrastructure
PostgreSQL, SQLite, schema design, query optimization
Data extraction, transformation, loading, and validation workflows
Schema validation, data quality checks, anomaly detection
Visualization & Reporting
Tools for communicating data insights
Dashboards, DAX measures, report publishing
Interactive dashboards, calculated fields, storytelling
Used in: Falcon 9 Prediction interactive dashboard
Used in: Prop Trading Dashboard P&L curves
Tools & Platforms
Development tools and deployment platforms
Version control, branching strategies, CI/CD workflows
Primary environment for data exploration and model prototyping
Used in: Prop Trading Dashboard, Maevie PM
Used in: Prop Trading Dashboard desktop app
Build tooling for modern web applications