Curriculum
The study program is structured according to the following curriculum. Further information can be found in the
Module 1: Data Scientist’s Toolbox | ||
| Module | ECTS | Topics |
Introduction into Data Science | 1.5 days, 2 ECTS | Introduction into Data Science including an overview of all courses, |
Data processing, visualisation, reproducibility and presentation | 2.5 days, 4 ECTS | Reading in and processing data in R, visualisation of data structures, setting up reproducible analyses. |
Module 2: Statistical Modelling | ||
| Module | ECTS | Topics |
Regression models | 2.75 days, 4 ECTS | Linear and non-linear regression, introduction into variable selection, introduction into regularised regression models, model goodness, re-sampling methods, implementation in R. |
Generalisied additive models | 2.75 days, 4 ECTS | Polynomial functions for modelling influence variables in regression models, splines, non-parametric models, implementation in R. |
Bayesian statistics | 2.75 days, 4 ECTS | Bayes theorem, Bayesian regression, Markov Chain Monte Carlo methods and Gibbs Sampling, implementation in R. |
Module 3: Machine Learning | ||
| Module | ECTS | Topics |
Supervised Learning | 2.75 days, 4 ECTS | Regularised models, variable selection, neuronal nets, decision trees and random forests, bagging and boosting |
Unsupervised Learning | 2.00 days, 4 ECTS | Clustering, dimensionality reduction, introduction into deep learning, generative models |
Module 4: Practical application | ||
| Module | ECTS | Topics |
Applied Data Science | 2.75 days, 4 ECTS | Practical application of the methods learned in the first three modules by analysing a data set in groups |
Projekt work | 3 months, 8 ECTS | Independent project work and evaluation of a data set including presentation and the creation of a statistical report. |