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All questions concerning the subject/degree:Data Science and Machine Learning - Master's Programme
Please note!
This degree programme is currently being planned for the winter semester 2025/2026.
The introduction of the degree programme is subject to the accreditation decision by the Accreditation Council and the publication of the decision in the Official Notices of the University of Oldenburg.
Orientation and Goals
The new 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 develop a comprehensive understanding of developing, implementing, and analysing data-driven algorithms on both technical and conceptual levels.
Students will experience a high proportion of guided but independent research directly in the laboratories of the university.
Students acquire the following specialist and interdisciplinary skills:
- Knowledge of data science/machine learning methods and their fundamentals
- Ability to analyse problems, compare and select methods for data driven solution
- Ability to formalise problems mathematically, develop and implement solutions and interpret their results
- Knowledge of ethical, legal and security-related boundaries
- Knowledge of data management and infrastructure
- Expertise in the presentation and discussion of data
- Expertise in scientific reading and writing
- Ethical reflection and professional behaviour/self-understanding, knowledge of good scientific practice
- Interdisciplinary knowledge, thinking and communication
- Ability to communicate scientifically (especially with people from outside the field)
- Ability to conduct independent research, as well as project and time management
Study Design and Contents
The programme consists of 42 ECTS credits in core modules, 48 ECTS credits in a specialisation and 30 ECTS credits for the Master's thesis.
The methodological foundations are taught in core modules, which are taken by all students and will lay ground for the later choice of a specialisation. They are divided into a compulsory area (30 ECTS credits) and a compulsory elective area (12 ECTS credits).
Following the core modules, students choose one of the three specialisations. Each specialisation includes a mandatory group project (12 ECTS credits).
Core modules (42 ECTS credits)
Compulsory modules (30 ECTS credits)
- Introduction to Data Science (6 ECTS credits)
- Applied Deep Learning (6 ECTS credits)
- Machine Learning (6 ECTS credits)
- Statistical Learning (6 ECTS credits)
- Interdisciplinary Lecture Series Data Science & Data Ethics (6 ECTS credits)
Compulsory elective modules (12 ECTS credits from those listed below)
- Exploring Research Data Management (6 ECTS credits)
- Trustworthy Machine Learning (6 ECTS credits)
- Machine Learning II (6 ECTS credits)
- Advanced Topics in Applied Deep Learning (6 ECTS credits)
- Time Series Analysis (6 ECTS credits)
- Introduction to IT-Security (6 ECTS credits)
- Designing Explainable Artificial Intelligence (6 ECTS credits)
- Applied AI- Multimodal-Multisensor Interfaces I: Foundations, User Modelling, and Common Modality Combination (3 ECTS credits)
- Applied AI - Multimodal-Multisensor Interfaces III: Language Processing, Software, Commercialisation, and Emerging Directions (3 ECTS credits)
- Internship (6 ECTS credits)
- Current topics in Data Science and Machine Learning (6 ECTS credits)
- Interdisciplinary language module for the recognition of German language or Academic English courses (6 ECTS credits)
Specialisation in Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences
Compulsory modules (18 ECTS credits)
- Theoretical Foundations of Machine Learning and Data Science (6 ECTS credits)
- Group Project Theoretical Foundations of Machine Learning in Maths and Natural Sciences (12 ECTS credits)
Compulsory elective modules (18 ECTS credits from the following + additional 12 ECTS credits from the core area)
- Mathematical Foundations of Statistical Learning (6 ECTS credits)
- Introduction to Numerical Methods for Partial Differential Equations (6 ECTS credits)
- Computational Physics (6 ECTS credits)
- Modelling of Complex Systems (6 ECTS credits)
- Current Topics in Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences (6 ECTS credits)
- Information Processing and Communication (6 ECTS credits)
Specialisation in Data Science and Machine Learning in Medicine and Health Care
Compulsory modules (30 ECTS credits)
- Medical Data Pipelines (6 ECTS credits)
- Medical Data Analysis with Deep Learning (6 ECTS credits)
- Big Data Analytics and Clinical Decision Support (6 ECTS credits)
- Group Project Data Science in Medicine and Healthcare (12 ECTS credits)
Compulsory elective modules (18 ECTS credits from the following)
- Special Topics in ‘Medical Informatics’ II (6 ECTS credits)
- Medical Technology (6 ECTS credits)
- Medical Basics (6 ECTS credits)
- Bioinformatics & Omics (6 ECTS credits)
- Current Topics in Data Science in Medicine and Healthcare (6 ECTS credits)
Specialisation in Data-Driven Speech and Hearing Sciences
Compulsory modules (30 ECTS credits)
- Digital Signal Processing (6 ECTS credits)
- Hearing and Communication Acoustics (6 ECTS credits)
- Algorithms for Speech Processing (6 ECTS credits)
- Group Project Data-Driven Speech and Hearing Sciences (12 ECTS credits)
Compulsory elective modules (18 ECTS credits from the following)
- Information Processing and Communication (6 ECTS credits)
- Introduction to Neurophysics (6 ECTS credits)
- Processing and Analysis of Biomedical Data (6 ECTS credits)
- Human Computer Interaction (6 ECTS credits)
- Current Topics in Data-Driven Speech and Hearing Sciences (6 ECTS credits)
Integrated internships
The ‘Internship’ module (6 ECTS credits) 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.
Focus Areas
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’
Reasons for Studying
- 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, healthcare, hearing science) and learn how to address data-bound problems in these domains
- Developing 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.)
Foreign Language Skills
German language skills are not required for admission.
In order to study this course at the University of Oldenburg, you need an adequate knowledge of English.
English Language Proficiency see admissions regulations
- Common European Framework of Reference for Languages (CEFR) Level B2
- Validity of language tests: Level C1 is valid for 6 years, level B2 is valid for 2 years
- if applicants are native speakers or they have a university degree obtained in an EU country or a country with English as the official language (within the last two years)
The proof of language proficiency must be presented for the enrolment.
For other proof possibilities see: Language requirements
Study abroad
In the core and in all three specialisations, three modules (6 ECTS credits each) are integrated for the recognition of an optional study abroad in the third semester. The group project can also be performed abroad.
Careers and Areas of Employment
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.
Target Group/Admission Requirements
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 ECTS credits (900 hours) in mathematics and computer science including at least
- 20 ECTS credits in mathematics, of which
- 5 ECTS credits in analysis or linear algebra and
- 5 ECTS credits in probability theory or statistics and
- 10 ECTS credits in computer science, of which
- 5 ECTS credits in the field of algorithms and
- 5 ECTS credits in a higher programming language (preferably Python).
- 20 ECTS credits in mathematics, of which
Students without a degree in the fields of data science, mathematics, statistics, physics, computer science, or business informatics must prove an additional 15 ECTS credits (450 hours) in data science. Competencies in data science can also be proven with work experience in the field.
If students can prove 20 ECTS credits in mathematics and 10 ECTS credits in computer science and do not miss more than 5 ECTS credits in the areas of statistics and a maximum of 5 ECTS credits in algorithms or programming, they may catch up on missing competencies in an additional module.
Students will be admitted based on a ranking order. The admissions committee will evaluate the applicant based on the documents presented. The degree of eligibility depends upon the sum of the points from categories A and B. The maximum number of points is 6.
Category A
Grade average of qualified Bachelor's degree
1.00 to 1.5 4 points
1.51 to 1.75 3.5 points
1.76 to 2.0 3 points
2.01 to 2.25 2.5 points
2.26 to 2.5 2 points
2.51 to 2.75 1.5 points
2.76 to 3.0 1 point
For the conversion of marks from abroad, see:
https://uol.de/en/students/recognition/abroad
Category B
Further points can be obtained through a relevant professional or scientific activity in the field of data science or machine learning (work experience, internships, bachelor's thesis; at least 3 months full-time work) - 1 point per activity, max. 2 points in total.
These qualifications are evaluated by the admissions committee.
Documents to be included in the application
The following documents must be enclosed with the application in German or English. (Documents in other languages will need to be accompanied by certified translations):
- Bachelor's degree and transcript of records (certified for applications via the university)
- proof of the number of semesters studied thus far (for applications via the university)
- completed specific eligibility form (to be found on course website and in application portal)
- proof of mastery of English (see language requirements)
- if applicable,
- certificates concerning relevant internships or work experience (e.g. employer's reference, internship certificate, supervisor's certificate)
- the subject of the Bachelor's thesis
We do not ask for letters of recommendation or letters of motivation!
Application/Admission Procedures
This course of studies accepts a limited number of applicants, and application is only possible in the winter semester.
Detailed application deadlines for the winter semester:
- Applications with a German university degree: by 15 July
- Applications from the EU: by 15 July
- Applications from third countries (non-EU): by 30 April
Overview application deadlines Master's programmes
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International applicants: Please note the different application procedures.