Statistics Lab

Team

Prof. Dr. Verena Wolf (lecturer)
Pascal L. Held, Pascal Schumann (assistants)

Course Goals

This course serves as an introduction to the fundamentals of statistics and probability theory and covers most of the core principles of modern data analysis and predictive modeling:

• Descriptive Statistics and Visualizations in Python
• Probabilities and Discrete Random Variables
• Continuous Random Variables and Laws of Large Numbers
• Parameter Estimation
• Statistical Testing
• Computer Simulations and Monte Carlo Methods
• Stochastic Processes

Format

The course follows an inverted classroom approach and is divided into three modules:

1. Descriptive Statistics, Probability & Random Variables
2. Distributions & Parameter Estimation
3. Statistical Testing

Each week consists of:

• Monday lecture (GHH, Prof. Wolf): a large group session covering the week’s material, including worked examples from the exercise sheet.
• Tutorials: small group sessions to work on the previous week’s exercises, discuss solutions, and take mini-tests.
• Self-study with ALA: practice exercises with ALA, an AI chatbot tailored to the course content that guides you through problems and answers your questions.
• Videos and materials: new learning materials for self-study, released weekly.

To be admitted to the final exam, students complete the exercises and mini-tests in the tutorials and submit several dialogues with ALA.

Organization

All information and materials are available in the CMS.