Information for those interested in the Master's Program

Master's program

In the Master's program Statistics and Data Science, the basic scientific concepts and methods of statistics are deepened and expanded as a basis for responsible data analysis. Due to its strong research-oriented focus, the completed degree program enables students to further develop and redesign suitable statistical methods even in very complex application situations. During their studies, students further choose to deepen their understanding with respect to one of five focus areas: Machine Learning, Biostatistics, Social Science and Data Science, Econometrics or Methodology and Modeling.

Additional information for enrolled students

If you want to learn more about the concrete lectures and specialization possibilities, you can visit the page with Information for enrolled students in the current available master's program (here you can find also the study regulations and the module catalogue)

Application

(portal opens on March 15th for Winter Semester/September 15th for Summer Semester)
For students without LMU user ID
For students with LMU user ID

Timeline

Applicants who neither have German citizenship nor are considered as "Bildungsinländer" must additionally submit an application for admission to studies to the LMU International Office. Acceptance from the international office does not imply acceptance to the program.
	Winter semester	Summer semester
Application portal opens	March 15th	September 15th
Strict application deadline	May 15th	November 15th
Result of first stage, test will be sent	End of May/ Beginning of June	End of November/ Beginning of December
Results of second stage	Early July	Early January
Selections interviews	Early/ middle July	Early/middle January
Application deadline at the international office	July 15th	January 15th
Official start of the semester	October 1st	April 1st
Start of courses	Mid-October	Mid-April

Requirements

The following are considered prerequisites for prospective master's students.

Statistical Modelling: Linear and generalized linear regression, interpretation of additive models; regularization (Ridge, Lasso); model selection (AIC, BIC); principal component analysis

Statistical Inference: point and interval estimators; maximum likelihood including asymptotic statements, likelihood inference in GLMs; priors and posteriors including predictive posterior; principles of statistical testing, testing based on ML estimators, important tests, multiple testing

Principles of Machine Learning: Risk minimization; classification, regression in machine learning context, LDA, QDA, naive Bayes, KNN; evaluation, resampling, standard performance metrics, ROC analysis; decision trees, fundamentals of neural networks; fundamentals of hyperparameter tuning

Fundamentals of probability theory: Kolmogorov's axioms; random variables, moments; definition of discrete and continuous distributions, joint and marginal distributions, definition and properties of conditional expectations, covariance and correlation; concepts of convergence, laws of large numbers, central limit theorem

Linear algebra: abstract vector spaces, basis systems; linear functions, kernel, range, invertibility; inner products, orthogonality, projections; eigenvalues, eigenvectors, spectral decomposition; quadratic forms, SVD, Cholesky decomposition

Analysis: convergence, sequences, series; continuity, differentiability; convexity, open and closed sets; differentiation in R^d, integration in R^d; Taylor series and Taylor approximation; basics of nonlinear optimization problems

Statistical software and statistical programming: Proficiency in a statistical programming language (R or python); programming paradigms (functional/OOP/imperative); control flow, vectorization, iteration; functions & data types; basic algorithms; error handling & debugging; version control; data visualization; Familiarity with a standard ML toolkit in R or Python and some applied ML experience

The following sources cover most of the knowledge above:

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/ The first 10 chapters of I2ML cover the BSc part.
Anton, H., & Rorres, C. (2013). Elementary linear algebra: applications version. John Wiley & Sons.
Peter Philip (2024). Calculus I and Calculus II for Statistics students. Lecture Notes, LMU Munich.
Grimmett, G. and Stirzaker, D.: Probability and Random Processes. Oxford University Press, 2001, Chapters 1-4 and 7

Selection process

This is a non-binding outline. Only the text of the „Satzung über das Eignungsverfahren für den Masterstudiengang Statistics and Data Science an der Ludwig-Maximilians-Universität München (PDF, 106 KB)“ is valid.

Bachelor's degree (180 ECTS) in Statistics or Data Science as a major, minor, or focus.
At least 150 ECTS (equal to 5 semesters) must be proven by the time of application, final degree can be submitted later.
English proficiency B2 (or English degree).

The application will be assessd by grade in the previous studies (up to 5 points) and knowledge in three areas:

Methods of statistical learning and modeling (up to 6 points), e.g. statistical inference, linear and generalized models, statistical and machine learning, introductory courses do not count
Mathematical foundations of statistics (up to 5 points), e.g. probability theory, calculus of matrices, analysis, numerics
Statistical software and statistical programming (up to 4 points), e.g. R, python, practical experience

A list of relevant lectures (PDF, 25 KB) (PDF, 25 KB) is provided for guidance.

Decision according to achieved points

18-20 Direct admission
10-17 Second stage
0-9 No suitability

Applicants without the necessary prerequisites can (for example) apply for the Bachelor Statistics and Data Science to acquire missing knowledge.

If you are eligible for the second stage, we will send you a written test.
You have two weeks to upload the solutions.
If the test is passed, you will be invited to an selection interview.

The interview will take 15 minutes
Knowledge in the three areas will be discussed

Enrollment

General information on enrollment

If you are coming from abroad you additionally have to register here:

https://www.lmu.de/en/study/degree-students/registration/index.html

Student Counseling

For individual questions about the Master's program please reach out to Lea Höhler. master.consultancy@stat.uni-muenchen.de