The Master of Science in Statistics at the University of Wisconsin–Madison is designed to provide rigorous training in statistical inference, data modeling, and computational methods. Students gain the skills needed for advanced data analysis in academic, industrial, biomedical, and governmental settings, and can specialize in areas such as machine learning, biostatistics, or statistical computing.
Predictive Modeling of Student Performance Using Logistic Regression
Bayesian Inference in Medical Diagnosis Applications
Survival Analysis of Patient Data in Cancer Studies
Design of Experiments for Agricultural Field Trials
Hierarchical Models for Multi-Level Educational Data
Analysis of Variance (ANOVA) in Clinical Trial Results
Markov Chain Monte Carlo Techniques for Parameter Estimation
Clustering Techniques for Consumer Segmentation
Time Series Forecasting of Stock Market Returns
Statistical Quality Control in Manufacturing Processes
Principal Component Analysis for Dimensionality Reduction
Handling Missing Data with Multiple Imputation Techniques
Evaluating Classifier Performance Using ROC Curves and AUC
Nonparametric Tests for Behavioral Research
Text Mining and Sentiment Analysis Using NLP Models
Outlier Detection in Environmental Monitoring Datasets
Random Forest Modeling for Credit Risk Assessment
Statistical Genetics: GWAS Analysis for Complex Traits
Bootstrap Methods in Confidence Interval Estimation
Spatial Statistics for Disease Mapping and Outbreak Detection
Equip yourself with quantitative skills to interpret data, inform decisions, and lead analytical projects across industries.
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