Data Science and Machine Learning, MSc University of Oldenburg

The Data Science and Machine Learning programme concentrates on data science research activities with a focus on life and natural sciences, including medicine.

Students in the programme acquire professional and interdisciplinary skills to meet the challenges of digital transformation in society and at the university. They master the methodological foundations of complex data analysis with a strong focus on machine learning methods, and they develop a comprehensive understanding of developing, implementing, and analysing data-driven algorithms on both technical and conceptual levels. The programme enables students to gain specific expertise in applying analytical methods across three specialisation areas and effectively communicate insights to domain experts. We offer the following three specialisations:

• Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences
• Data Science and Machine Learning in Medicine and Health Care
• Data-Driven Speech and Hearing Sciences

Students will experience a high proportion of guided but independent research directly in the laboratories of the university.

Reasons to study Data Science and Machine Learning

• Get to know, apply and develop state-of-the art machine learning methods across a broad variety of different data modalities
• Specialise in one of three areas of specialisation (theoretical foundations, health care, hearing science) and learn how to address data-bound problems in these domains
• Develop expertise that is sustainable and relevant to society
• English-taught programme with many international students
• Interdisciplinary background of teachers and students
• Small groups with 30 students per year
• Optional integrated language courses and internship
• Extensive support structures (tutorials, learning workshops, etc.)

Career perspectives

Graduates will be excellently qualified for specialist and management positions in various fields of activity involving the collection, management, processing, analysis and interpretation of digital data, as well as for academic research. Possible career fields include:

• data scientist with a focus on data analysis and model development and validation
• data analyst specialising in data cleaning and preparation
• data engineer specialising in the development and management of data pipelines
• machine learning engineer specialising in the selection, adaptation and further development of machine learning (including deep learning) methods for various information processing tasks

Contacts with companies and start-ups will also be promoted.

Core 30 CP

• Introduction to Data Science
• Applied Deep Learning
• Machine Learning
• Statistical Learning
• Interdisciplinary Lecture Series Data Science & Data Ethics

Core Electives: choose 12 CP

• Exploring Research Data Management
• Trustworthy Machine Learning
• Machine Learning II
• Advanced Topics in Applied Deep Learning
• Time Series Analysis
• Introduction to IT Security
• Designing Explainable Artificial Intelligence
• Applied AI – Multimodal-Multisensor Interfaces
• Internship
• Current topics in Data Science and Machine Learning
• German / Academic English

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Specialisation: Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences

Compulsory 18 CP

• Theoretical Foundations of Machine Learning and Data Science
• Group project

Electives: choose 18 CP + additional 12 CP from the core area

• Mathematical Foundations of Statistical Learning
• Introduction to Numerical Methods for Partial Differential Equations
• Computational Physics
• Modelling of Complex Systems
• Current Topics in Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences
• Information Processing and Communication

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Specialisation: Data Science and Machine Learning in Medicine and Health Care

Compulsory 30 CP

• Medical Data Pipelines
• Medical Data Analysis with Deep Learning
• Big Data Analytics and Clinical Decision Support
• Group project

Electives: choose 18 CP

• Special topics in "Medical Informatics"
• Medical Technology
• Medical Basics
• Bioinformatics & Omics
• Current Topics in Data Science in Medicine and Health Care

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Specialisation: Data-Driven Speech and Hearing Sciences

Compulsory 30 CP

• Digital Signal Processing
• Hearing and Communication Acoustics
• Algorithms for Speech Processing
• Group project

Electives: choose 18 CP

• Information Processing and Communication
• Introduction to Neurophysics
• Processing and Analysis of Biomedical Data
• Human Computer Interaction
• Current topics in Data-Driven Speech and Hearing Sciences

Master's thesis: 30 CP

Total: 120 CP

The Internship module (6 CP) in the compulsory elective area of the core area enables a professional internship lasting 180 hours, in which students experience data science and machine learning in practical application. The internship can take place at public institutions, private companies, scientific institutions and other organisations in Germany or abroad.

Applicants are eligible for admission if they have completed a Bachelor's degree of at least 180 ECTS credits (three year full-time study) in the fields of data science, mathematics, statistics, physics, computer science, business informatics or a closely related field. All applicants must prove the following upon application:

• 30 credit points (900 hours) in mathematics and computer science including at least
  • 20 credit points in mathematics, of which
    • 5 credit points in probability theory or statistics
    • 5 credit points in analysis or linear algebra 
  • 10 credit points in computer science, of which
    • 5 credit points in the field of algorithms
    • 5 credit points in a higher programming language (preferably Python)

Students without a degree in the fields of data science, mathematics, statistics, physics, computer science, or business informatics must prove an additional 15 credit points (450 hours) in data science. Competencies in data science can also be proven with work experience in the field.

If students can prove 20 credit point in mathematics and 10 credit points in computer science and do not miss more than five credit points in the areas of statistics and algorithms/programming, they may catch up on missing competencies in an additional module.

Please note that one ECTS credit point equals 30 hours of work including courses, preparation, self-study and exams.

Students will be admitted based on a ranking order determined by the grade of their Bachelor's degree and points for additional qualifications: 

• Relevant professional or scientific activity in the field of data science or machine learning (work experience, internships, Bachelor's thesis; at least three months full-time work)

Details on the application and ranking procedure can be found on the course website.

We do not ask for letters of recommendation or letters of motivation!

Approx. 435 EUR per semester

You should expect to spend about 1,000 EUR per month to cover personal expenses (accommodation, health insurance, food).

Students are permitted to work alongside their studies. If you are a student from a non-EU country, you are allowed to work 140 full or 280 half workdays per year.

We offer many student jobs. These jobs are usually offered (once the students have studied here for a few months) in the areas of tutorials and research assistant work.

Please keep in mind that finding a student job is much easier if you speak some German.

Check funding opportunities here: https://uol.de/p15263en.

• Individual study and career counselling by the programme coordinator
• Study and Career Counselling Service of the university
• Career Day of the university

The student union ("Studierendenwerk") owns and runs several different student accommodation buildings around the city. Although student rooms in Germany are typically rented unfurnished, the "Studierendenwerk" also provides furnished rooms for international students at affordable rates. They also help with finding private accommodation for international students.

There are also several ways to find private accommodation including:

• local newspapers, e.g. the NWZ
• Websites like www.wg-gesucht.de, where apartment listings can be narrowed down by desired criteria, such as number of flatmates or facilities available
• The "Schwarzes Brett" (notice board) on Stud.IP and several boards in the student canteen / "Mensa" building on campus (if you are still looking once you arrive in Oldenburg)

The University of Oldenburg was founded in 1973, making it one of Germany‘s youngest universities. Its goal is to find answers to the major challenges society faces in the 21st century – through interdisciplinary, cutting-edge research.

The pathways on the Oldenburg campus are short: the university's academic staff and administrative staff work closely together, using an interdisciplinary approach. Many are integrated into special research areas, research groups, and European clusters of excellence.

The university cooperates closely with more than 300 other universities worldwide and is also affiliated with non-university institutes in the areas of research, education, culture, and business.

The University of Oldenburg prepares approximately 15,000 students for professional life. It offers a broad range of disciplines, from language studies, cultural studies, and the humanities to educational sciences, art and musicology, the economic and social sciences, mathematics, computer science, and the natural sciences as well as the medicine and health science programmes.

The development of the excellent research performance of Oldenburg has started to attract the Max-Planck, Helmholtz, Leibniz, and Fraunhofer Associations, which have established research groups and junior researcher groups in Oldenburg particularly in the last few years.

The University of Oldenburg is a centre of research with national and international appeal. All academic fields have developed concise criteria for excellence, organised according to quality, effectiveness, efficiency, and originality and in compliance with international standards. The university creates space for scientific networking within the university and beyond. Interdisciplinary and social responsibility are identity-forming hallmarks of research in Oldenburg. Scientific networking also leads to an efficient transfer of research results in the programmes.

Oldenburg looks back on more than 900 years of history and is now, with its +176,000 inhabitants, a centre of cultural, scientific and economic life in the region of North-West Germany.

Oldenburg is a good place to live. A mix of entertainment and culture – bars, restaurants and trendy venues as well as theatres and a number of museums – create a big-city feeling. Yet it is a safe place where you can get around easily on your bicycle, as most students do. The city has a well-developed bus system. Student fees cover public transport in the city itself and all over Germany. The green city is also directly connected to the European rail network.

For further information, please visit the following website: https://www.oldenburg.de/.