Our Teaching Method
Two Parallel Tracks
Technical Foundation
You'll work with Python libraries that process shipping manifests, weather data, and traffic patterns. We start with simple decision trees—if congestion rises at Tanjong Pagar, reroute through Pasir Panjang. Then move to neural networks that spot patterns humans miss.
Real datasets from container terminals. Messy data with missing timestamps and conflicting records. Because that's what you'll face when implementing these systems. Students build predictive models using historical freight data spanning three years of regional operations.
Industry Application
Theory means nothing if you can't deploy it. We focus on systems that actually run—monitoring truck locations, predicting delivery windows, optimizing warehouse picking routes. These aren't classroom exercises. They're based on challenges our partner companies solved last year.
You'll understand why an AI recommendation gets ignored by experienced warehouse managers and how to build trust in automated systems. The human side matters as much as the algorithms. Maybe more, honestly.
What You'll Actually Build
Route Optimization Engine
Train models on thousands of completed deliveries. Factor in time-of-day traffic, vehicle capacity, driver shift patterns. Your algorithm suggests routes that save 15 minutes per stop—which compounds across a fleet of 40 trucks.
Demand Forecasting System
Combine sales history with external signals—weather forecasts, local events, economic indicators. Predict inventory needs two weeks out. Not perfectly, but well enough to reduce stockouts by a meaningful margin while cutting excess inventory costs.
Warehouse Flow Analyzer
Process sensor data from picking operations. Identify bottlenecks in real-time. Suggest layout changes based on product movement patterns. Some of our students caught inefficiencies that had persisted for years simply because no one had the tools to visualize the data properly.
Ready to Start
Next Cohort Enrolling Now
Our programs run eight to ten months. Classes meet twice weekly in the evenings, designed for working professionals. You'll need basic programming knowledge—Python fundamentals—and genuine curiosity about logistics operations.
We cap enrollment at 24 students. Small enough that instructors know your name and understand your specific industry challenges. Large enough for diverse perspectives when you're debugging why your model keeps overfitting on holiday shipping patterns.