Lecture 1 - Introduction & Python Setup

Management Science

About this Course

About Me

• Field: Optimizing & simulating complex systems
• Languages of choice: Julia, Python and Rust
• Interests: Modeling, simulations, machine learning
• Teaching: OR, algorithms, programming & mathematics
• More: tobiasvlcek.com

I really appreciate active participation and interaction!

What is

Management Science?

Photo by Patrick Konior on Unsplash

Management Science

Management science is an interdisciplinary field that applies research-based methods, such as modeling, statistics, and algorithms, to solve complex organizational problems and guide strategic decisions, seeking optimal or near‑optimal outcomes across institutions, corporations, and governments.¹

It is not the science of management!

Course Outline

• Part I: Python Foundation (Lectures 1-3)
  • Variables, loops, functions, data science tools
• Part II: Management Science Tools (Lectures 4-9)
  • Monte Carlo, Scheduling, Routing, Metaheuristics
• Part III: Consulting Competition (Lectures 10-12)
  • Client briefings, development, final presentations

You’re now consultants learning to solve real business problems!

From Amazon to Hospitals

Where Algorithms Make Millions

• Amazon: Routing algorithms save millions in delivery costs
• Uber: Real-time matching algorithms connect drivers and riders
• Hospitals: Scheduling algorithms optimize shifts and OR usage
• Airlines: Revenue algorithms price tickets dynamically
• Manufacturing: Production scheduling reduces waste and delays

Management Science is everywhere!

This Course Approach

The Consultant Mindset

• You’ll work on business problems
• Learn to communicate solutions to non-technical clients
• Build practical tools that solve actual challenges
• Compete in a consulting competition with real scenarios

Think like a consultant: What value does my solution bring to the business?

Course Structure

• 12 lectures of intensive learning
• 3 hours per lecture (with breaks!)
• Interactive format:
  • Concepts & Setup
  • Hands-on Python notebooks
  • Consulting Competition
• Final competition: Apply everything you’ve learned

Each lecture builds on the previous - attendance is important!

Grading

Grade Composition:

• Course has 100 points, 50 needed to pass
• 2 Assignments: 30 points each
• Final Consulting Project: 40 points

We will have several competitions including our final competition, where you will have the chance to earn bonus points!

Assignments

Assignment Strategy:

• Start with easy warm-ups in tutorials
• Progress to real-world problems
• All assignments have clear rubrics
• Bonus points for creative solutions!

Late submissions: -10% per day (max 3 days)

Learning Python

• In my experience, the best way to learn is by doing!
• Here, we will focus on decision algorithms
• You will start to learn Python by doing the tutorials

Don’t worry, I will help you out if you have any questions!

What to Expect

• No prior programming required - we start from zero!
• But experience is helpful!
• Fast-paced but with lots of support
• Practical focus - less theory, more doing
• Mistakes are welcome - they’re how we learn

The course gets easier after week 3 - the hardest part is getting started!

Course Goals

By the end, you will be able to:

1. Write Python code to solve business problems
2. Apply algorithms for scheduling, routing, and optimization
3. Simulate uncertainty using Monte Carlo methods
4. Present solutions like a management consultant
5. Build tools that create business value

You’ll leave with a portfolio of ideas to work on real solutions!

Why Python?

• Origins: Conceived in late 1980s as a teaching and scripting language
• Simple Syntax: Python’s syntax is mostly straightforward and very easy to learn
• Versatility: Used in web development, data analysis, artificial intelligence, and more
• Community Support: A large community of users worldwide and extensive documentation

Help from AI

• You are allowed to use AI in the course
• I use it as well (e.g., Claude, ChatGPT, Gemini, …)
• These tools are great for learning Python!
• Can help you a lot to get started with programming
• I will also teach you how to use it effectively

But you should not simply use them to replace your learning.

Learning to Program

My Recommendation

1. Be present: Attend the lecture and solve the tutorials
2. Put in work: Repeat code and try to understand it
3. Do coding: Run code, play around, modify, and solve
4. Compete: Participate in the competitions to learn

Great resources to start are books and small challenges. You can find a list of recommendations in the literature recommendations.

Don’t give up!

Programming is like learning to ride a bike

• You’ll fall a few times
• It feels impossible at first
• Then suddenly… it clicks!

Lectures 1-3 are the hardest. Push through and it gets much easier!

Setting up Python

What is an IDE?

• Integrated Development Environment = application
• It allows you to write, run and debug code scripts
• Other IDEs include for example:
  • PyCharm from JetBrains
  • Zed

Install VS Code

• Download and install from the website
• Built for Windows, Linux and Mac
• Install the Python and Jupyter extension
• Great! First steps are done

Unsure on how to work with VS Code and notebooks? Ask me! I’m happy to help you out!

Installation of Python with uv

• We will use uv to install and manage Python versions
• It works on Windows, Mac and Linux
• It helps us to manage packages and virtual environments
• Now, we all go here and install uv and Python

If the installation does not work, let me know!

Notebooks with uv

Quick Check

• Have you installed uv and initialized the project?
• Great! Before we continue, check the following:
  • You have a folder for the course
  • You have initialized uv with uv init inside the folder
  • You can see a file called pyproject.toml in the folder

Something not working yet? Ask me!

Using Notebooks

• Now we need to add a kernel to our project
• Run uv add --dev ipykernel from your terminal
• Now run uv add jupyter in the terminal
• This allows us to use uv Python in notebooks
• Done? Perfect. Now we can start!

Working with Notebooks

• Now you can download the files from the website
• Just click on one of the sessions and open it
• Select Jupyter on the right side
• Download and save the files to your course folder
• Open them and select “Open with Jupyter Notebook”

That was the hardest part today!

Any questions

so far?

What’s Next?

After the break, we’ll dive into:

• Tutorial 1: Variables and basic data types
• Tutorial 2: Lists and loops
• Tutorial 3: Conditionals and control flow

Ready for the tutorials? Make sure your Jupyter notebook is working before we continue!

The End

That’s it for our introduction!

Make sure you have:

• VS Code installed and running
• Python environment set up with uv
• Jupyter notebooks working
• The tutorial files downloaded

Take a 10-minute break, then we’ll start with the interactive notebooks.

Literature

Literature on Algorithms

• Christian, B., & Griffiths, T. (2016). Algorithms to live by: the computer science of human decisions. First international edition. New York, Henry Holt and Company.²
• Ferguson, T.S. (1989) ‘Who solved the secretary problem?’, Statistical Science, 4(3). doi:10.1214/ss/1177012493.

Books on Programming

• Downey, A. B. (2024). Think Python: How to think like a computer scientist (Third edition). O’Reilly. Here
• Elter, S. (2021). Schrödinger programmiert Python: Das etwas andere Fachbuch (1. Auflage). Rheinwerk Verlag.

Think Python is a great book to start with. It’s available online for free. Schrödinger Programmiert Python is a great alternative for German students, as it is a very playful introduction to programming with lots of examples.

More Literature

For more interesting literature, take a look at the literature list of this course.

Footnotes

1. An Introduction to Management Science: Quantitative Approaches to Decision Making (15 ed.). Boston: Cengage Learning, Inc. 2019. ISBN 978-1-337-40652-9.

2. A great inspiration to learn more about Algorithms!