MSc Statistics and Data Science
In the selection interview, the expertise, the approaches to the discussion of problems and the coherence of the argumentation with regard to the requirements for the Master program are examined. These requirements include knowledge of essential statistical methods and procedures, in particular statistical modelling and machine learning, their mathematical foundations as well as the practical application of statistical methodology in statistical software.
You will find a list of relevant topics and recommended literature below. You do not not need to show proficiency in all topics (and all literature), but the potential and the abilities to fill gaps.
The interview will typically start at your previous knowledge (eg. thesis or practical experiences) and your motivation. From that, we will explore your abilities and skills in statistical modelling and inference and your mathematical foundations.
- The selection interview can be held in English or German on request of the applicant. The interview will take approximately 25 minutes.
- The committee will give a decision immediately after the interview. The committee may discuss options with you on how to catch up on knowledge, both in the case of admission and in the case of rejection.
- The application to the master programme can be repeated once.
Content and Literature
Methods of statistical learning and modelling
- Statistical modelling: simple and multiple linear regression, binary and Poisson regression, forecasting, hypothesis testing, model selection
- Inference: point and interval estimators, maximum likelihood and Bayesian estimation, principles of statistical testing, testing based on ML estimators, important tests
- Principles of machine learning: supervised and unsupervised learning, classification, risk minimization, regularization, evaluation, trees, forests, other important methods
Literature
- G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning. MIT Press, 2010.
- Held, L, Sabanés Bové, D.: Likelihood and Bayesian Inference. Springer 2020, Chapters 1-7.
- Fahrmeir, L., Kneib, Th., Lang, S., Marx, B.D.: Regression. Springer 2021, Chapters 1-4
- Bischl, B. et al.: Introduction to Machine Learning (I2ML). https://slds-lmu.github.io/i2ml/
Mathematical foundations of statistics