Welcome to Management Science!
Management Science
Course Description
Management Science is an interdisciplinary field that applies scientific methods to organizational problem-solving and decision-making. By leveraging mathematical modeling, statistics, and numerical algorithms, management science helps businesses achieve their strategic goals effectively.
In this course, you’ll build a comprehensive toolkit by solving real problems across diverse business domains. Each algorithm is a tool, each case is a client, and each presentation is a pitch. Throughout the semester, you’ll work with realistic, business-relevant scenarios using Python. The course end in a consulting competition where teams tackle client briefs (food delivery routing, healthcare staff scheduling, or inventory optimization) and present solutions to a panel of “executives.”
Learning Outcomes
By the end of this course, you will be able to:
Technical Skills:
- Implement Management Science solutions in Python
- Work effectively with NumPy and Pandas for analysis and modeling
- Apply Monte Carlo simulation to model uncertainty and risk
- Build and evaluate forecasting models for demand and time series
- Design and analyze scheduling solutions with key performance metrics
- Solve routing problems using heuristics and local search improvements
- Handle multi-objective trade-offs and combine decision criteria meaningfully
- Understand and apply metaheuristics for complex optimization problems
Professional Skills:
- Collaborate effectively in small teams (3-4 students)
- Communicate technical insights with clear visualizations and compelling narratives
- Present solutions in a consulting-style format to business stakeholders
- Approach complex problems with structured analytical thinking
This course is specifically designed for business students. No prior programming experience required, the teaching format supports different skill levels so every student can progress effectively.
Course Structure
The course is organized into three distinct parts across 12 lectures:
- Part I: Python Foundation (Lectures 1-3)
- Part II: Management Science Tools (Lectures 4-9)
- Part III: Consulting Competition (Lectures 10-12)
Grading
- Assignment 1: Risk & Forecasting (Due Lecture 8) - 30%
- Assignment 2: Full Optimization Toolkit (Due Lecture 10) - 30%
- Final Competition: Client Project (Lectures 10-12) - 40%
- Win mini-competitions during lectures (Lectures 4-9)
- Best client project in Part III chosen by peer teams
Resources & Support
Required Tools
- Laptop capable of running Python and Jupyter notebooks
- Python (installed via uv package manager in class)
- GitHub Copilot (free with Student Developer Pack)
- VS Code or similar IDE
Getting Help
- During class: Ask questions immediately, others likely have the same question
- Team support: Leverage your group for collaborative problem-solving
- Email: Response within 48 hours for urgent questions
AI Policy
Level 1: Pause – Use of AI defined by the educator
A course chatbot is available on the learning website for exploratory study. It is designed to guide your problem-solving process rather than provide answers directly. Use it as a learning tool, not a solution generator.
You may also use external AI tools (e.g., ChatGPT, Claude, Mistral, Gemini). However:
- Please be careful and try to understand the code generated.
- Relying on AI to solve tasks for you weakens your own learning.
- AI should ideally support understanding — not replace practice.
- Using AI without understand the code can lead to security risks.
Questions & Contact
If you have any questions regarding the course, please contact me at vlcek@beyondsimulations.com.
Contributors
Thanks to Asvin Goel, who inspired part of this course.