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19330401 Statistics for Data Science

Winter Term 2021/2022

lecture and exercise by Dr. Henri Elad Altman


Time and place

  • Lecture: Wednesdays 08:00-10:00h, SR 046, Takustr. 9, in person (+streamed online)
  • Exercise: Tuesdays 08:00-10:00h, fully online

  • Final Exam:  Date to be announced in due course.


Prerequisits: basic set theory (inclusion, union, intersection, difference of sets), basic analysis (infinite series, calculus), matrix algebra, some knowledge of probabilistic foundations (discrete probability, Gaussian distributions) would be helpful.

If you are an FU student you only need to register for the course via CM (Campus Management).
If you are not an FU student, you are required to register via MyCampus/Whiteboard.

Course Overview/ Content:

This course serves as an introduction to foundational aspects of modern statistical data analysis. Frequentist and Bayesian inference are presented from the perspective of probabilistic modelling. The course will consist of three main parts:

  1. Probability foundations: probability spaces, random variables, distribution of a random variable, expectation and covariance, important limit theorems and inequalities 
  2. Frequentist inference: point estimators, confidence intervals, hypothesis testing. 
  3. Bayesian inference: conjugate inference, numerical models, data assimilation.

Teaching material will be Recorded lectures, Handwritten notes and gappy notes, Lecture notes, Weekly Exercise Sheets. Please look into Whiteboard for teaching material.

References

  • Larry Wasserman: All of Statistics, a concise course in statistical inference
  • DeGroot and Schervish: Probability and statistics, 4th edition
  • José M. Bernardo, Adrian F.M. Smith: Bayesian Theory
  • Leonhard Held and Daniel Sabanés Bové: Applied Statistical inference, likelihood and Bayes
  • Sebastian Reich and Colin Cotter: Probabilistic forecasting and Bayesian Data Assimilation