Management-Science/index.qmd at main · beyondsimulations/Management-Science · GitHub

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---
title: "Welcome to Management Science!"
subtitle: "Management Science"
html: default
---

# 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

::: callout-note
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%

::: callout-tip
- 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

1. **During class:** Ask questions immediately, others likely have the same question
2. **Team support:** Leverage your group for collaborative problem-solving
3. **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:

1. Please be careful and try to understand the code generated.
2. Relying on AI to solve tasks for you weakens your own learning.
3. AI should ideally support understanding — not replace practice.
4. Using AI without understand the code can lead to security risks.

# How to Navigate This Course

- **Slides:** Click "RevealJS" in the top right corner of each lecture page
- **Notebooks:** Interactive exercises accompany each lecture

You can find detailed information about course policies, grading rubrics, and expectations in the [syllabus](general/syllabus.qmd).

# Questions & Contact

If you have any questions regarding the course, please contact me at [[email protected]](mailto:[email protected]?subject=ManagementScience).

# Contributors

Thanks to [Asvin Goel](https://github.com/rajgoel), who inspired part of this course.