Columbia University’s M.S. in Statistics prepares students to lead data-driven investigations across domains such as finance, healthcare, economics, and tech. These project topics are designed to apply core statistical theory to modern real-world challenges in prediction, inference, and decision science.
Bayesian Inference in Small Sample Clinical Trials
Forecasting Electricity Demand Using ARIMA and LSTM Models
Survival Analysis of Cancer Patients Using Cox Proportional Hazards Model
Credit Scoring Models Using Logistic Regression and AUC Evaluation
Time Series Anomaly Detection in Financial Markets
Monte Carlo Simulation of Random Walk in Portfolio Optimization
Causal Inference in Observational Studies Using Propensity Score Matching
Statistical Modeling of Climate Change Indicators
Hierarchical Models for Disease Incidence by Region
Regression Analysis for Housing Price Predictions
Markov Chain Monte Carlo for Posterior Estimation in Bayesian Models
Statistical Analysis of Customer Churn in Subscription Businesses
Dimensionality Reduction in High-Dimensional Genomics Data
Bootstrap Resampling Techniques for Confidence Interval Estimation
Predictive Modeling for Student Dropout Rates Using Educational Data
Mixed Effects Models for Repeated Measures in Health Studies
Real-Time Sports Analytics Dashboard Using R and Shiny
Inference and Estimation for Big Data Using Apache Spark
Poisson Regression for Modeling Traffic Accident Frequency
Evaluation of Statistical Assumptions in Experimental Design
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