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The core module "Statistics" covers fundamental statistical concepts and methods. It consists of two courses, "Statistical Reasoning and Inference" and "Sampling and Experimental Design". The core module "Informatics" gives an overview of the steps of the knowledge discovery process and consists of two courses, "Knowledge Discovery and Data Mining" and "Big Data Management".
The "Individual Modules" comprise, for example, lectures on statistical modelling, multivariate data analysis, advanced programming, and database systems in regression modelling and multivariate data analysis. The module "Human Computation and Analytics" covers those aspects of data science in which humans either produce data and process and analyse it with the help of algorithms or in which data is presented to humans by a computer system. Models such as decision trees, neural networks, support vector machines, and ensembles are introduced in the module "Predictive Modelling".
Technical, legal, and ethical issues of data security are key issues, especially when dealing with personal data or when planning experiments in Data Science. These topics are covered in the module "Data Ethics and Data Security". In the "Elective Modules", students may choose courses in specialised fields from the Master's courses in statistics, informatics, and computer linguistics. The module "Data Science Practical" plays a central role in the curriculum of the Master's programme. Students will tackle real-world problems in cooperation with industrial partners. Summer schools, focused tutorials, and the DataFest are part of the module "Current Research in Data Science".
The "Individual Modules" comprise, for example, lectures on statistical modelling, multivariate data analysis, advanced programming, and database systems in regression modelling and multivariate data analysis. The module "Human Computation and Analytics" covers those aspects of data science in which humans either produce data and process and analyse it with the help of algorithms or in which data is presented to humans by a computer system. Models such as decision trees, neural networks, support vector machines, and ensembles are introduced in the module "Predictive Modelling".
Technical, legal, and ethical issues of data security are key issues, especially when dealing with personal data or when planning experiments in Data Science. These topics are covered in the module "Data Ethics and Data Security". In the "Elective Modules", students may choose courses in specialised fields from the Master's courses in statistics, informatics, and computer linguistics. The module "Data Science Practical" plays a central role in the curriculum of the Master's programme. Students will tackle real-world problems in cooperation with industrial partners. Summer schools, focused tutorials, and the DataFest are part of the module "Current Research in Data Science".
Educational organisation
The Master's programme Data Science is an interdisciplinary programme. It is carried out jointly by the Department of Statistics and the Institute for Informatics at LMU Munich.The curriculum is a modularised study programme. It comprises lectures, exercise classes, lab courses, and practicals. It also includes summer schools and focused tutorials.
Semesters 1-2: Core modules Statistics and Informatics; Individual Modules (Fundamentals of Data Science); Predictive Modelling; Human Computation and Analytics.
Semesters 2-3: Data Ethics and Data Security; Elective Modules; Current Research in Data Science.
Semester 3: Data Science Internship.
Semester 4: Master's thesis and defence.
Study abroad unit(s)
New exchange programmes with international universities that offer data science programmes are being established. Students have the opportunity to go abroad for short research visits or for a full semester. They may also do their internships abroad.Internships
In the module "Data Science Practical", students cooperate with industrial partners and research facilities in data analysis projects. This may include internships at the partner organisations.Forms of assessment
Forms of assessment include written and oral exams, oral presentations, and written reports.Course objectives
Students will be highly qualified, responsible academics who can work as data scientists in industry, economy, and science.Language requirements
Proficiency in EnglishAcademic requirements
Bachelor of Science (or equivalent) in statistics or informatics or related disciplines
Want to improve your English level for admission?
Prepare for the program requirements with English Online by the British Council.
- ✔️ Flexible study schedule
- ✔️ Experienced teachers
- ✔️ Certificate upon completion
📘 Recommended for students with an IELTS level of 6.0 or below.
Enrolment fees
Approx. 120 EUR per semester. The fee includes a semester ticket covering public transport in the Munich metropolitan area.Costs of living
Living costs (including accommodation and health insurance) in Munich range from 800 to 1,000 EUR per month.Arrival support
Orientation day with information about the curriculum and general organisational issuesServices and support for international students
The International Office offers a support and integration programme to assist international students, doctoral students, and postdocs at LMU:http://www.en.uni-muenchen.de/about_lmu/contact/int_office/index.html
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