Master of Science in Data Science

STAT 510 · Probability and Statistical Inference · 3 credits · Fall

Covers probability theory, estimation, hypothesis testing, and linear models. Students use R to fit and diagnose models on real data.

STAT 520 · Statistical Learning · 3 credits · Fall

Covers regression, classification, resampling, and regularization from a statistical viewpoint. Assignments compare methods by prediction error and interpretability.

CS 560 · Machine Learning · 3 credits · Fall, Spring

Covers supervised and unsupervised learning, model selection, regularization, and evaluation. Students train and tune models in Python on real datasets.

DATA 510 · Data Engineering · 3 credits · Fall

Covers data pipelines, warehousing, and workflow orchestration. In a weekly computing lab students ingest, clean, and store data from several sources.

DATA 520 · Data Visualization · 3 credits · Spring

Covers perceptual principles, chart design, and interactive dashboards. Students build visualizations in the weekly computing lab and present them for critique.

DATA 530 · Data Ethics and Governance · 3 credits · Summer

Discusses privacy, bias, consent, and accountability in the collection and use of data. Students analyze case studies and draft governance policies.

STAT 530 · Bayesian Data Analysis · 3 credits · Spring

Covers prior and posterior reasoning, hierarchical models, and Markov chain Monte Carlo. Students fit Bayesian models to applied problems and report their uncertainty.

STAT 540 · Time Series and Forecasting · 3 credits · Fall, Spring

Covers autocorrelation, ARIMA models, seasonality, and forecast evaluation. Students build forecasts for economic and sensor data.

DATA 540 · Large-Scale Data Processing · 3 credits · Fall

Covers distributed storage and parallel processing frameworks for datasets too large for one machine. Students write and tune jobs on a shared cluster.

CS 570 · Natural Language Processing · 3 credits · Spring

Covers tokenization, language models, parsing, and text classification. Students build a working text-analysis system from raw corpora.

CS 580 · Deep Learning · 3 credits · Spring

Covers neural network architectures, optimization, and training at scale. A weekly computing lab has students train convolutional and sequence models on GPU nodes.

CS 585 · Computer Vision · 3 credits · Fall

Covers image formation, feature detection, object recognition, and video analysis. Assignments apply both classical methods and learned models to image data.

DATA 598 · Data Science Capstone · 6 credits · Fall, Spring, Summer

Student teams analyze real data for an industry, government, or research partner. Each team presents its findings and a written report in the final semester.