Stanford’s M.S. in Statistics equips students with powerful tools in probability, inference, machine learning, and data analysis. These research projects explore real-world applications in medicine, tech, finance, and social science through rigorous statistical methodologies and computational modeling.
Bayesian Hierarchical Modeling for Disease Spread Analysis
Survival Analysis in Clinical Trials with Censored Data
Causal Inference Using Propensity Score Matching in Healthcare Studies
Time Series Forecasting for Renewable Energy Demand
Anomaly Detection in Financial Transactions Using GMM
Modeling Customer Churn Using Logistic Regression and Random Forest
Design and Analysis of A/B Tests for Web Platforms
Statistical Simulation of Election Forecast Models
Text Classification Using Naive Bayes and TF-IDF
Bayesian Optimization for Hyperparameter Tuning in ML
Generalized Linear Models for Insurance Risk Prediction
Dimensionality Reduction for Genomic Data with PCA & t-SNE
Multivariate Regression for House Price Forecasting
Gaussian Process Regression for Environmental Monitoring
Data Imputation Techniques in Large-Scale Surveys
Machine Learning Interpretability with SHAP Values
Predictive Analytics for Student Performance in MOOCs
Bayesian Networks for Modeling Patient Diagnoses
Advanced Bootstrap Techniques for Small Sample Analysis
Monte Carlo Methods for Estimating Uncertainty in ML Models
From statistical modeling to reproducible analytics pipelines, Collexa supports Stanford Statistics students with R, Python, SAS, and thesis-level guidance.
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