The UNC BS in Data Science equips students for high-performance computing, AI research, and quantitative problem-solving. A calculus-heavy core in linear algebra, probability, and optimization underpins advanced courses in deep learning, cloud architecture, and MLOps. Students train models on GPU clusters, deploy microservices on Kubernetes, and contribute to open-source libraries. Industry capstones with RTP tech giants and UNC Health ensure real-world relevance and job-ready portfolios.
Federated-learning framework predicting hospital readmissions
Graph-neural-network model for protein-interaction mapping
Real-time anomaly detection pipeline for smart-grid sensors
Apache Spark ETL workflow processing terabyte-scale satellite imagery
Automated ML platform benchmarking hyperparameter-search algorithms
Edge-AI object-detection model for autonomous drones
Differential-privacy library for civic-data releases
Synthetic-data generator improving fraud-detection training sets
Reinforcement-learning agent optimizing urban traffic lights
Financial-time-series prediction using transformer architectures
Open-source CLI tool simplifying model-card documentation
Kubernetes operator managing GPU resource scheduling
Dashboard monitoring carbon footprint of large-scale training jobs
Quantum-computing exploration of combinatorial optimization
White paper on ethical considerations in emotion-recognition AI
Hackathon toolkit accelerating computer-vision prototyping
Benchmark suite for evaluating LLM toxicity mitigation strategies
Design, train, and deploy intelligent systems that scale to real-world data volumes.
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