The Master of Science in Data Science at the University of Michigan offers students an interdisciplinary curriculum focused on real-world data analysis, machine learning, statistics, and computer science. Graduates are equipped to transform data into insights across sectors such as healthcare, business, and public policy.
Real-Time Fraud Detection Using Graph Neural Networks
Predictive Analytics for Student Dropout in Online Learning Platforms
Designing Scalable Pipelines for Big Data Processing on Cloud Platforms
Early Disease Diagnosis Using Deep Learning on Electronic Health Records
Reinforcement Learning Models for Dynamic Pricing in E-Commerce
Bias Detection and Mitigation in Predictive Algorithms
Modeling Social Media Influence Using Temporal Graphs
Multi-Modal Sentiment Analysis for Customer Feedback Systems
Anomaly Detection in Industrial IoT Systems Using Autoencoders
Development of Explainable AI Models for Financial Risk Assessment
Federated Learning for Privacy-Preserving Data Collaboration
Churn Prediction in Telecom Using Ensemble Learning Methods
Forecasting Renewable Energy Output Using Time-Series Models
AI-Driven Optimization of Supply Chain Networks
Natural Language Processing for Legal Document Classification
Clustering and Visualization of Genomic Data Using t-SNE and UMAP
Automated Feature Engineering for Large-Scale ML Pipelines
Bayesian Inference for Health Risk Prediction
Real-Time Traffic Congestion Forecasting Using Deep RNNs
Evaluating Fairness Metrics Across Demographic Subgroups in ML Models
This program bridges computing, statistics, and domain expertise to prepare students for data-driven innovation in academic, corporate, and public sectors.
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