Northwestern’s MS Stats grounds students in measure-theoretic probability, high-dimensional inference, and Bayesian computation. Labs in R and Python explore causal forests, network models, and survival analysis, while consulting practicums place students on interdisciplinary teams with med-school researchers and hedge-fund quants. A professional communication sequence turns complex intervals into actionable insights for stakeholders.
Develop Bayesian hierarchical models predicting electoral turnout with spatial random effects
Implement sparse factor analysis for gene-expression dimensionality reduction
Create an R Shiny app visualizing competing-risk survival curves for transplant outcomes
Design an adaptive clinical-trial simulation comparing dose-escalation rules
Apply causal-impact models assessing marketing campaigns on web traffic
Quantify network community uncertainty via stochastic block-model bootstrapping
Build an automated MCMC diagnostic suite leveraging deep kernel learning
Analyze text sentiment volatility and its Granger causality with crypto prices
Derive minimax rates for non-parametric regression under heteroskedastic errors
Construct probabilistic graphical models for electricity load forecasting
Evaluate fairness constraints in uplift modeling for targeted interventions
Optimize hyper-parameters of Gaussian processes with Bayesian optimization
Estimate excess mortality during pandemics via Poisson mixed models
Simulate adaptive survey designs minimizing bias in hard-to-reach populations
Write an R package implementing doubly robust estimators for panel data
Turn rigorous inference into real-world impact with Northwestern Statistics.
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