CMU’s Stats & Data Science BS provides elite quantitative training infused with real-world impact. Students forecast pandemics with public-health researchers, optimize MLB player lineups in the Sports Analytics Club, and critique biased algorithms in the Deliberative Data course. Whether you intern at Google, the CDC, or a stealth fintech, you’ll graduate fluent in R, Python, and causal inference.
Bayesian multilevel model estimating vaccine-hesitancy clusters across counties
Reinforcement-learning engine allocating ICU beds under surge scenarios
Shiny dashboard monitoring micro-mobility usage with real-time sensor feeds
Random-forest churn predictor for subscription-based ed-tech startup
Causal impact analysis of ride-hailing on public-transit ridership
Hierarchical topic model of global climate-policy news coverage
Survival analysis of employee turnover in hybrid-work environments
Deep generative model synthesizing realistic medical images for privacy
AB-testing framework evaluating UI dark-mode effects on engagement
Markov Chain Monte Carlo estimator for baseball pitch-selection strategies
Graph-based anomaly detection in power-grid telemetry data
Ethics audit quantifying fairness metrics in credit-risk scores
Time-series forecast of renewable generation using probabilistic neural nets
Interactive educational tool visualizing sampling distributions and p-hacking
Spatial statistics model mapping opioid-overdose hotspots with EMS data
Data pipeline benchmarking open-source ETL tools on petabyte-scale logs
Semi-supervised learning approach classifying coral bleaching severity
LSTM prediction of air-quality index leveraging meteorological covariates
Text mining of SEC filings to anticipate market volatility
Sports draft value calculator integrating player similarity metrics
Turn complex data into clear decisions at CMU’s powerhouse Stats & DS program.
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