Course Syllabus
Management Science
Course Structure & Schedule
Part I: Python Foundation (Lectures 1-3)
No mini-competitions yet - focus on solid foundations
Lecture 1: Welcome to Management Science
- Course introduction
- Python setup with uv package manager
- Variables, data types, basic operations
- Lists and indexing fundamentals
- Conditionals and basic loops
Lecture 2: Python Programming Advances
- Functions for code organization
- Dictionaries for structured data
- Tuples and multiple return values
- Sorting and optimization fundamentals
- GitHub Copilot integration and best practices
Lecture 3: Data Science Foundation
- NumPy for numerical computing and random simulation
- Pandas for data manipulation and analysis
- Data visualization with matplotlib
- CSV file handling and real dataset exploration
- Integration practice with business scenarios
Part II: Management Science Tools (Lectures 4-9)
Mini-competitions begin - apply algorithms to business problems
Format for Lectures 4-9:
- Hour 1: Solution presentations of previous competition
- Hour 2: Interactive lecture on core concepts
- Hour 3: Hands-on notebook practice + class discussion
- Hour 4: Mini-competition with real data
- Bonus Points: Best solution teams earn points toward final grade
Lecture 4: Dealing with Uncertainty - Monte Carlo Simulation
- Probability distributions and random sampling
- Business risk modeling techniques
- Portfolio optimization under uncertainty
- Coffee shop simulation case study
Lecture 5: Forecasting the Future
- Time series analysis fundamentals
- Demand forecasting methods
- Forecast evaluation metrics
- Seasonal and trend analysis
Lecture 6: Smart Quick Decisions in Scheduling
- SPT (Shortest Processing Time) rule
- EDD (Earliest Due Date) rule
- Gantt chart visualization
- Performance metrics: makespan, tardiness, flow time
Lecture 7: Better Routing - Local Search & Improvements
- Nearest neighbor heuristic
- 2-opt local search improvements
- Route optimization metrics
- Real logistics applications
Lecture 8: Tough Trade-offs - Multi-Objective Optimization
- Weighted scoring methods
- Pareto efficiency concepts
- Decision criteria combination
- Business trade-off analysis
Lecture 9: The Metaheuristics Toolkit - Beyond Greedy
- When simple heuristics fail
- Genetic algorithms introduction
- Simulated annealing basics
- Algorithm selection strategies
Part III: Consulting Competition (Lectures 10-12)
Real client challenges with professional presentations
Lecture 10: Client Briefings
- Three client projects to choose from:
- QuickBite: Food delivery routing optimization
- NurseNext: Healthcare staff scheduling
- TechMart: E-commerce inventory optimization
- Team formation and data exploration
- Project scope definition
Lecture 11: Development Sprint
- Presentation skills training
- Intensive solution development
- Peer consultation and feedback
- Prototype completion milestone
Lecture 12: The Consulting Finals
- Professional presentation competition
- “Executive panel evaluation”
- Solution demonstration and Q&A
Assessment & Grading
Grade Composition (100 points total)
| Component | Points | Percentage | Description |
|---|---|---|---|
| Assignment 1: Risk & Forecasting | 30 | 30% | Due Lecture 8 |
| Assignment 2: Optimization Toolkit | 30 | 30% | Due Lecture 10 |
| Final Competition Project | 40 | 40% | Lectures 10-12 |
| - Solution Quality | 20 | 20% | Technical implementation |
| - Presentation | 20 | 20% | Communication effectiveness |
Bonus Opportunities (Additional points possible)
- Mini-competition victories (Lectures 4-9): up to +10 points
- Peer-selected best client project: +5 points
Late Work Policy
- Assignment 1 & 2: -10% per day late (up to 3 days)
- Competition project: No late submissions accepted (real consulting deadline!)
- Extensions granted only for documented emergencies