UVA’s MS in Statistics equips students with the analytical skills to tackle real-world challenges in health, business, tech, and public policy. Core areas include regression, Bayesian inference, experimental design, machine learning, and statistical computing using R and Python. Graduates excel in data science, actuarial analysis, biostatistics, or doctoral research.
Time series forecasting of hospital bed occupancy using ARIMA and Prophet
Bayesian modeling for predictive marketing in e-commerce
Survival analysis for oncology patient treatment outcomes
Monte Carlo simulations for election polling accuracy
Multivariate regression for real estate price prediction
Machine learning ensemble models in fraud detection
Hierarchical modeling in education performance benchmarking
Principal Component Analysis (PCA) on socioeconomic indicators
Statistical modeling of infectious disease outbreaks using SEIR structures
Text sentiment analysis using logistic regression and Naïve Bayes
Non-parametric testing in nutritional epidemiology
Time-to-failure modeling for manufacturing reliability analysis
A/B testing strategies for digital ad effectiveness
Clustering algorithms for customer segmentation in retail
Variable selection and LASSO regression in genomic studies
Risk factor modeling for chronic disease using survey data
Forecasting electricity demand using generalized additive models
Classification trees for clinical diagnostic decision tools
Meta-analysis of intervention studies in public health
Develop in-demand data and analytics skills with UVA’s applied MS in Statistics.
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