The University of Chicago’s M.S. in Analytics program merges quantitative analysis with strategic thinking. These project topics guide students in solving data-driven problems in marketing, finance, healthcare, supply chains, and technology through actionable analytics and advanced modeling.
Churn Prediction Model for Subscription-Based Businesses
Customer Segmentation Using K-Means and PCA
Real-Time Fraud Detection in Financial Transactions
Sales Forecasting with Ensemble Learning Methods
Sentiment Analysis on Customer Reviews Across Platforms
Building a Data Warehouse for Retail Chain Optimization
Supply Chain Risk Modeling Using Bayesian Networks
Developing a Dashboard for Real-Time Marketing KPIs
Using NLP to Automate Customer Support Ticket Categorization
Optimizing Ad Spend with Attribution Modeling
Predictive Maintenance for Manufacturing Equipment Using Sensor Data
Pricing Strategy Optimization Using Conjoint Analysis
Social Media Analytics for Campaign Performance Tracking
Healthcare Cost Analysis Using Regression and Time Series Forecasting
Recommendation Systems for E-commerce Personalization
Data Pipeline Automation with Apache Airflow and BigQuery
Predicting Student Dropout Using Educational Data Mining
A/B Testing Framework for Feature Rollouts in SaaS Products
Retail Footfall Prediction Using External Event Data
Ethical AI: Detecting and Mitigating Bias in Predictive Models
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