Duke’s BS in Statistical Science empowers students to wrestle uncertainty at scale—from Bayesian disease-spread models to deep-learning reproducibility audits. Core labs deploy R, Python, and Stan on Duke’s GPU cluster, while industry practicums with RTP biotechs and fintechs hone stakeholder storytelling. Ethics modules interrogate algorithmic bias, and a capstone ‘Data4Good’ competition channels teams into public-interest analytics with open-data partners.
Bayesian hierarchical model forecasting dengue outbreaks using weather covariates
Causal-impact analysis of minimum-wage hikes on retail employment
Interactive Shiny dashboard benchmarking NCAA player efficiency
Explainable boosting machine detecting credit-card fraud patterns
Spatial point-process study of urban gun-violence hotspots
Tensor-decomposition of fMRI time-series for depression biomarkers
Synthetic-control evaluation of city bike-lane rollouts on traffic injuries
Deep-ensemble uncertainty quantification in autonomous-vehicle perception
Federated-learning pipeline preserving privacy in hospital readmission prediction
Multi-armed-bandit optimization of email donation campaigns for NGOs
Survival-analysis of startup lifecycles across sectors
NLP topic modeling of SEC filings for systemic-risk signals
Adaptive survey design reducing non-response bias in polarizing topics
Open-source curriculum teaching tidyverse principles with Duke campus data
Policy brief on differential-privacy standards for municipal open-data portals
Reinforcement-learning algorithm balancing grid-battery dispatch under uncertainty
Meta-analysis visual app summarizing vaccine-trial efficacy
GitHub action auto-checking reproducibility of student projects
Turn data into trustworthy decisions with Duke’s powerhouse Statistical Science major.
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