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Course Goals

This course serves as an introduction to the fundamentals of statistics and probability theory and will cover 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


Lecture Modalities:

  • Weekly recorded on-site lecture (Mo. 8:30-10 am, not on April 15th). Video course and lecture notes as supplementary online resources.
  • First three weeks of term only: Additional recorded on-site lecture (Tue. 8:30-10 am). The first lecture is on April 16th.
  • Written on-site exam on the 8th of July, 8:00-10:00
  • Written minitests administered in the lecture every second session
  • Graded exercise sheets to be prepared in groups of 3-4 students
  • Students are invited to form groups until April 28. All registered students that have not found a group will be assigned a group on April 29.
  • Exam admission is tied to achieving a total score of at least 50% for a combined score on minitests and exercise sheets (2/3 minitests, 1/3 exercise sheets)
  • Exam bonus for students that score >90% on the combined score
  • On-site tutorials (Thu 8:30-10am, Thu 10:15am-12pm, Thu 14:15-15:45pm or Fri 14:15-15:45)



The registration for this course is open from April 15, 8:30am to April 19, 11:59pm. When registering you will have to state your preferences regarding the time of your tutorial as well as your preferred language. Adapted to your demand, we will offer tutorials in German, English and bilingual, where the main language is English, but questions may also be asked in German.

General Information

  • The first lecture will take place on Tuesday the 16th of April at 8:30 am (GHH lecture hall).
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