Northwestern’s MS in Analytics condenses a PhD-level tool-kit—statistical learning, optimization, time-series forecasting, and cloud-scale data engineering—into an intensive three-quarter sprint. Industry practicums embed students in cross-functional teams at leading firms such as Allstate, Abbott, and the Chicago Cubs, delivering board-ready models that unlock millions in value. A parallel leadership seminar sharpens client-facing communication, ethics of algorithmic equity, and product-management acuity.
Predict hospital readmissions with gradient-boosting and SHAP explainability
Optimize dynamic surge pricing for ride-sharing fleets via reinforcement learning
Deploy real-time anomaly detection on streaming IoT turbine sensors
Forecast SKU-level demand with Prophet and hierarchical Bayesian models
Cluster social-media engagement to refine micro-influencer targeting strategy
Build a computer-vision pipeline classifying recycling contamination on conveyor belts
Quantify impact of paywall tweaks on digital-news subscriber LTV using uplift modeling
Design a fairness audit of mortgage algorithms for disparate-impact compliance
Develop an NLP pipeline extracting adverse-event signals from clinical notes
Construct a graph-analytics engine revealing supply-chain fragility nodes
Create a Monte Carlo simulator optimizing hedge ratios for carbon futures
Implement automated feature-store architecture for MLOps at scale
Visualize city mobility patterns with kepler.gl to inform transit-equity policy
Prototype an AutoML dashboard enabling marketers to A/B test creatives overnight
Run sentiment-volatility co-integration studies on crypto-asset Twitter chatter
Master advanced analytics and drive data-powered decisions across industries.
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