• Get to know, apply and develop state-of-the art machine learning methods across a broad variety of different data modalities
• Specialize in one of three areas of specialization (theoretical foundations, healthcare, 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.)

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

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.

The programme consists of 42 CP in core modules, 48 CP in a specialisation and 30 CP 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 CP) and a compulsory elective area (12 CP). 
Following the core modules, students choose one of the three specialisations. Each specialisation includes a mandatory group project (12 CP). 

Core modules (42 CP)

Compulsory modules (30 CP)
Introduction to Data Science (6 CP)
Applied Deep Learning (6 CP)
Machine Learning (6 CP)
Statistical Learning (6 CP)
Interdisciplinary Lecture Series Data Science & Data Ethics (6 CP)

Compulsory elective modules (12 CP from those listed below)
Exploring Research Data Management (6 CP)
Trustworthy Machine Learning (6 CP)
Machine Learning II (6 CP)
Advanced Topics in Applied Deep Learning (6 CP)
Time Series Analysis (6 CP)
Introduction to IT-Security (6 KP)
Designing Explainable Artificial Intelligence (6 CP)
Applied AI- Multimodal-Multisensor Interfaces I: Foundations, User Modelling, and Common Modality Combination (3 KP)
Applied AI - Multimodal-Multisensor Interfaces III: Language Pro-cessing, Software, Commercialisation, and Emerging Directions (3 KP)
Internship (6 KP)
Current topics in Data Science and Machine Learning (6 CP)
interdisciplinary language module for the recognition of German language or Academic English courses (6 KP)

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

Compulsory modules (18 CP)
Theoretical Foundations of Machine Learning and Data Science (6 CP)
Group Project Theoretical Foundations of Machine Learning in Maths and Natural Sciences (12 CP)

Compulsory elective modules (18 CP from the following + additional 12 CP from the core area)
Mathematical Foundations of Statistical Learning (6 CP)
Introduction to Numerical Methods for Partial Differential Equations (6 CP)
Computational Physics (6 CP)
Modelling of Complex Systems (6 CP)
Current Topics in Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences (6 CP)
Information Processing and Communication (6 CP)

Specialisation in Data Science in Medicine and Healthcare

Compulsory modules (30 CP)
Medical Data Pipelines (6 KP)
Medical Data Analysis with Deep Learning (6 CP)
Big Data Analytics and Clinical Decision Support (6 CP)
Group Project Data Science in Medicine and Healthcare (12 CP)

Compulsory elective modules (18 CP from the following)
Special Topics in ‘Medical Informatics’ II (6 CP)
Medical Technology (6 CP)
Medical Basics (6 CP)
Bioinformatics & Omics (6 CP)
Current Topics in Data Science in Medicine and Healthcare (6 CP)

Specialisation in Data-Driven Speech and Hearing Sciences

Compulsory modules (30 CP)
Digital Signal Processing (6 CP)
Hearing and Communication Acoustics (6 CP)
Algorithms for Speech Processing (6 CP)
Group Project Data-Driven Speech and Hearing Sciences (12 CP)

Compulsory elective modules (18 CP from the following)
Information Processing and Communication (6 CP)
Introduction to Neurophysics (6 CP)
Processing and Analysis of Biomedical Data (6 CP)
Human Computer Interaction (6 CP)
Current Topics in Data-Driven Speech and Hearing Sciences (6 CP)

Studying abroad
In the core and in all three specialisations, three modules (6 CP each) are integrated for the recognition of an optional study abroad in the third semester. The group project can also be performed abroad.

Integrated internships 
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 whose mother tongue is not English must produce a proof of English proficiency. English proficiency can be proven by a Bachelor's degree with English as the language of instruction from an EU country. Otherwise a certified proof of English language skills at a B2 level is needed (not older than two years). If you provide a proof of C1 level or higher, it may be at most 6 years old. A test from a language centre of a German university is accepted. The admissions committee can accept other evidence provided it demonstrates sufficient language qualification.

Further details on the English language requirements (including a reference table for the different tests) can be found on the university's website.

Knowledge of German is not necessary for admission. The university offers free language courses during the semester and during the semester breaks (as intensive courses). You can have German or academic English courses count as 6 credits towards your degree.

Where to apply?

International applicants (holders of a Bachelor's degree that was NOT issued in Germany): www.uni-assist.de

Holders of a German Bachelor's degree: application via the university

Academic 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, or business informatics. 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 analysis or linear algebra and            
      5 credit points in probability theory or statistics and
  • 10 credit points in computer science, of which
    • 5 credit points in the field of algorithms and
        5 credit points in a higher programming language (preferably Python).

If students can prove 20 credit point in mathematics and 10 credit points in computer science and do not miss more than 5 credit points in either the areas of statistics, 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://www.uni-oldenburg.de/en/students/recognition/conversion-foreign-grades/

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!

• The University of Oldenburg offers in-person teaching, which requires students to be present in Oldenburg.
• The programme is a full-time programme. Part-time studies can be arranged on an individual basis.
• Classes start mid-October.
• Lecture-free periods can be used for internships, independent study, or holidays:

summer break: beginning of August to mid-October

winter break: beginning of February to mid-April