Yale’s M.S. in Statistics and Data Science equips students with deep analytical, computational, and theoretical knowledge to tackle modern data challenges. Below are project and thesis ideas spanning applied and theoretical domains.
Bayesian Causal Inference in Healthcare Outcomes
Deep Generative Models for Tabular Data Imputation
Fairness-Aware Machine Learning in Hiring Algorithms
Multi-Armed Bandit Models for Real-Time Decision Making
Spatio-Temporal Analysis of Urban Traffic Using Big Data
Random Forest vs. XGBoost: Ensemble Model Benchmarking
Causal Graph Discovery Using Structural Equation Models
Survival Analysis in Longitudinal Clinical Studies
Time Series Forecasting Using LSTM and Prophet
Optimizing A/B Testing Using Bayesian Methods
Anomaly Detection in Credit Card Transactions
Meta-Analysis and Model Uncertainty in Medical Trials
Predictive Modeling of Customer Lifetime Value
Statistical NLP Techniques in Sentiment Dynamics
Clustering High-Dimensional Data with t-SNE and DBSCAN
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