MSc Data Science - University of Munich (LMU)
Applicant criteria
- No specific age required
- Both
Opportunity criteria
The University of Munich or Ludwig-Maximilians-Universität München (LMU) is offering a 2 years Master of Science (MSc) degree in Data Science. LMU Munich is the first university in Germany that offers an elite graduate program in Data Science in English.
Data Science is the science of extracting knowledge and information from data and requires competencies in both statistical and computer-based data analysis. The elite program Data Science is an interdisciplinary program and is carried out jointly by the Department of Statistics and the Institute for Informatics at LMU Munich. The program is part of and is supported by the Elite Network of Bavaria.
The curriculum of the elite master's program Data Science is a modularised study program. Students learn statistical and computational methods for collecting, managing, and analyzing large and complex data sets and how to extract knowledge and information from these data sets.
The program also comprises courses on data security, data confidentiality, and data ethics. In the practical modules, students will tackle real-world problems in cooperation with industrial partners. Other highlights of the program are the summer schools and the focused tutorials.
Upon graduation, students are well prepared for a career as a data scientist in the private or public sector in fields such as applied economics, political science, sociology, education, medicine, public policy, and media research. Students may also pursue a doctoral study in a variety of academic disciplines that require quantitative analysis.
Admission Requirements
- Bachelor of Science (or equivalent) in Statistics or Informatics or related disciplines (at least 180 ECTS or equivalent).
- Excellent knowledge in Informatics and Statistics. Applicants need to provide evidence of knowledge in the following fields:
- Statistical Science and Data-Based Modelling: This includes, in particular, statistics and topics such as data mining, probability theory, and machine learning (at least 30 ECTS or equivalent). (Average Grade 2)
- Computer Science and Computational Methods: This includes, in particular, data structures and algorithms, database systems, programming principles and practice, software engineering (at least 30 ECTS or equivalent). (Average Grade 3)
- Overall Average Grade must be better than 1.5.
- Proficiency in English: at least B2 CEFR (or equivalent); or English university entrance qualification; or first degree in English.
Candidates need to fulfill all the requirements if they want to apply.
Course Structure
The curriculum of the elite master’s program Data Science is a modularised study program. Students learn statistical and computational methods for collecting, managing, and analyzing large and complex data sets and how to extract knowledge and information from these data sets.
The program also comprises courses on data security, data confidentiality, and data ethics. Other highlights are the practical modules in which students will tackle real-world problems in cooperation with industrial partners, as well as summer schools and tutorials, and the DataFest.
With this training graduates of the master program will be innovative and responsible academics with excellent career opportunities both in industry and economy as well as in science and research.
Curriculum Overview
Statistics: Inference and Sampling (12 ECTS) - First and Second Semester
The core module Statistics covers fundamental statistical concepts and methods and consists of two courses.
Informatics: Knowledge Discovery and Big Data Management (12 ECTS) - First and Second Semester
The core module Informatics gives an overview of the steps of the knowledge discovery process and consists of two courses.
Fundamentals of Data Science (12 ECTS) - First Semester
Each student will be assigned to two courses from a variety of courses in advanced methods of statistics and informatics. These comprise lectures on statistical modeling, multivariate data analysis, advanced programming, and database systems, in regression modeling and multivariate data analysis, among others. At the end of the module, students will be on a homogeneous level of expertise in both statistics and informatics.
Human Computation and Analytics (9 ECTS) - First and Second Semester
The module Human Computation and Analytics covers those aspects of Data Science, in which humans either produce data, and process and analyze it with the help of algorithms, or in which data are presented to humans by a computer system.
Predictive Modelling (6 ECTS) - Second Semester
Predictive Modelling, in particular by means of non-linear, non-parametric methods, has become a central part of modern data analysis both in computer science and statistics in order to uncover complex patterns and relationships in data.
The module covers models such as decision trees, neural networks, support vector machines, and ensembles (random forest, bagging, boosting) and concludes with advanced techniques regarding model selection, feature selection, and hyperparameter optimization.
Data Ethics and Data Security (6 ECTS) - Second and Third Semester
The Data Ethics and Data Security module covers basic legal and ethical questions and challenges of data security.
Elective Modules (12 ECTS) - Second and Third Semester
In the elective modules, students may choose courses in specialized fields from the regularly offered master courses in statistics, informatics, and computer linguistics. In addition, students may also attend master-level courses at the partner universities. These include courses on image processing at TUM, computational finance at Augsburg University, and mathematical statistics at TUM.
Current Research in Data Science (9 ECTS) - Second and Third Semester
In this module, publications of current research in Data Science will be discussed. Students will learn to work independently with scientific publications and to present newly acquired scientific knowledge. This module also comprises the summer schools, the focused tutorials, and the DataFest.
Data Science Practical (12 ECTS) - Third Semester
The module Data Science Practical plays a central role in the curriculum of the master program. Practical experience with data-analytic methods that are taught in the core and elective modules is essential in order to generate knowledge from data.
Students will work on practical problems in the field of data science. The problems are typically concrete projects provided by non-university partners. The focus of the course is therefore not only on tackling methodological challenges in the analysis of massive data (Big Data) but on communicating the results and findings to the client.
Master thesis and disputation (30 ECTS) - Fourth Semester
The master thesis concludes the study program. The thesis may be either research-orientated or stimulated through a practical problem, e.g. as an extension of a data science practice.
Tuition Fees
There is no tuition fee for this program, but a fee for student services and the basic semester ticket.
In Germany, students don’t have to pay tuition fees for most degree programs. However, you might want to look for a student job (of which there are plenty) or apply for a scholarship to finance your studies in Munich.
Scholarships
As an international student, you are eligible for several scholarships and funding opportunities throughout Germany and the State of Bavaria. You can also count on the support of institutions like the Munich Student Union.
“Assistance in Case of Financial Difficulty” Scholarship
Eligibility:
International students and doctoral candidates at the LMU, who find themselves in short-term and unexpected financial need, can apply for one-time financial assistance ($731), funded by the State of Bavaria, at the International Office at LMU.
For the application you need the following documents:
Scholarship Application Deadline: 30 November