Oldenburg MSc Data Science and Machine Learning

What is University of Oldenburg MSc Data Science and Machine Learning?

Oldenburg’s MSc Data Science and Machine Learning is a 2 years, 120 ECTS programme. It brings core methods in data science together with chosen specialisation before independent final work. The full planning total is INR 28.61 lakh. For an Indian applicant, the first decision is whether the sequence matches prior study and intended work.

The official description confirms an on-campus academic sequence with taught study, choice and independent assessment. That balance defines this programme more clearly than its award label alone. The examination regulation controls credit recognition, module order and the final award.

How much does MSc Data Science and Machine Learning at the University of Oldenburg cost?

For planning, use EUR 25,832 plus INR 26,400, or about INR 28.61 lakh, across 24 months. The total uses the published programme fee, current semester contribution, the university's EUR 1,000 monthly living estimate, APS and the adult national visa fee.

The first-year blocked-account amount remains the student's living money. It is not added again above the full living plan. Converted at EUR 1 = INR 109.7500 using the European Central Bank reference rate for 3 September 2026. Flights, deposits, translations, uni-assist fees and bank spreads are excluded.

Proof of sufficient funds is required for the student visa.German Missions in India study visa funding rule

The Oldenburg semester contribution, fee schedule and ECB reference file support the calculation.

The current programme fee is EUR 0. Standard Oldenburg degree programmes have no tuition, but the published fee schedule names separate charges for three programmes in this cluster. The exact enrolment and programme invoice control the amount due.

The university publishes a EUR 1,000 monthly living estimate. This plan covers 24 months. Rent deposits, travel and personal spending can still move the cash requirement above it.

The Winter 2026/27 semester contribution is EUR 457.90. A later semester can change this charge, so the table is a planning base rather than a fee guarantee.

Rupee totals change with exchange rates even when euro charges do not. Part-time earnings are excluded because a job, work schedule and income are not guaranteed.

Can an Indian applicant enter University of Oldenburg MSc Data Science and Machine Learning?

An Indian application is credible when the transcript clearly proves 30 ECTS in mathematics and computing. Oldenburg evaluates degree content, accepted language evidence and programme documents separately. APS and detailed module descriptions help the admissions team interpret the qualification, but they don't replace a missing academic prerequisite.

The official admission page for MSc Data Science and Machine Learning gives the binding subject screen. 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: 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): We do not ask for letters of recommendation or letters of motivation! 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 5 ECTS credits in analysis or linear algebra and 5 ECTS credits in probability theory or statistics and 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). 5 ECTS credits in the field of algorithms and 5 ECTS credits in a higher programming language (preferably Python).

For this programme, the practical comparison is between completed modules and core methods in data science. A degree name on its own doesn’t establish that match, particularly when an adjacent discipline contains little of the named preparation.

An Indian file should keep original credits, grades and module descriptions visible. APS verifies the academic record, while the University of Oldenburg still makes its own admission decision for MSc Data Science and Machine Learning.

How should an Indian student apply for Oldenburg MSc Data Science and Machine Learning?

The selected intake is Winter 2027/28, with 30 Apr 2027 as the published planning deadline for this route. Indian degree holders normally apply through uni-assist and include APS evidence, unless the programme names a separate application channel. Start the document sequence well before the closing date.

Subject fit, APS, uni-assist or the named programme route, admission decision, then student visa

The allowances are Nbyula planning estimates, not processing times published by University of Oldenburg.

The exact programme application page controls the route. Applicants with an international degree use the course-specific Oldenburg route and normally submit through uni-assist. Indian qualifications require APS evidence.

The checked source supports The published planning deadline is 30 April 2027 for the selected winter route. Deadline treatment can differ by nationality, degree origin and entry semester, so do not substitute another Oldenburg programme’s date.

The complete file normally includes academic records, grading information, language evidence, identity documents and translations where applicable. Early submission leaves time for a document defect without changing the deadline.

Admission and the student visa are separate decisions. An offer does not guarantee a visa, housing or arrival before enrolment.

What jobs can follow University of Oldenburg MSc Data Science and Machine Learning?

The published curriculum most directly supports work as data scientist, machine learning engineer or data engineer. These are study-linked directions, not placement promises. Oldenburg doesn't publish an audited placement rate or salary distribution for this exact programme, so prior experience and competitive recruitment remain material.

For MSc Data Science and Machine Learning, the university publishes no exact placement rate or salary distribution for this programme. The legal job-search period is time to seek suitable work, not an employment promise.

The programme career description connects the course with these fields. 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: Contacts with companies and start-ups will also be promoted. 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

The route from core methods in data science to master’s thesis explains why data scientist is a defensible direction. It doesn’t show that every graduate receives that title, enters the same sector or earns the same salary.

After successful study, Germany’s Residence Act section 20 can permit up to 18 months for a qualifying job search when the graduate has sufficient funds. That period isn’t an employment, sponsorship or permanent-residence guarantee.

Who is University of Oldenburg MSc Data Science and Machine Learning for?

The strongest fit is a graduate whose prior study supports core methods in data science and whose intended work connects with data scientist. An adjacent-field applicant needs a written module-level ruling. A candidate missing the stated preparation should resolve that gap before paying for verification, application or relocation.

A strong shortlist starts with the academic sequence from core methods in data science to chosen specialisation. That sequence suits someone who wants to use the master’s for data scientist rather than only collect a broad award title.

An adjacent record needs a faculty ruling because Oldenburg assesses subject content. The application is stronger when every relevant module shows its original credit value, assessment and syllabus rather than relying on a self-created equivalence.

Someone whose goal sits outside data scientist, machine learning engineer, data engineer should compare a different syllabus. The programme’s value depends on wanting its actual academic route, not merely the University of Oldenburg name.

What does the University of Oldenburg MSc Data Science and Machine Learning curriculum contain?

The current published 120 ECTS curriculum is organised around Core methods in data science, Chosen specialisation, Specialisation group project, Master's thesis. Current regulations control sequencing and availability. Read the credit pattern as the course's academic balance before choosing a specialisation route, while the current regulations control sequencing and availability.

The note “Published curriculum area” applies to 4 components in this table.

• Complete the approved 120 ECTS programme
• Follow the examination regulations for compulsory and elective credits
• Module availability and sequencing can vary by semester

The published credit groups give MSc a distinct academic centre rather than a generic master’s label. In particular, Core methods in data science prepares the later master’s thesis, so the sequence cannot be reduced to a list of interchangeable electives.

The published groups include Core methods in data science, Chosen specialisation, Specialisation group project, Master’s thesis. Their credit values show how the programme balances required study, choice and independent work. An elective heading does not guarantee that every named class runs in a chosen term.

Module availability, sequencing and recognition remain subject to the current examination rules and semester catalogue. This is the curriculum decision that deserves attention before enrolment. The current examination regulations and module catalogue govern the final study plan, credit recognition and thesis route.

Should an Indian student shortlist University of Oldenburg MSc Data Science and Machine Learning?

Shortlist MSc Data Science and Machine Learning when core methods in data science matches both your academic record and intended work. The recommendation is conditional because Oldenburg still checks subject content, language proof and documents. Resolve the module fit in writing before paying any application or relocation cost.

The strongest case connects prior preparation in core methods in data science with a credible plan for data scientist. That is more specific than applying because the award title sounds related to a previous degree. The official course description supplies the academic detail. 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). 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. 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) 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) 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) 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) 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) 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) Digital Signal Processing (6 ECTS credits) Hearing and Communication Acoustics (6 ECTS credits) Algorithms for Speech Processing (6 ECTS. 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 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’ 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.) The current official module catalogue names inf040 Introduction to Data Science; inf5408 Angewandtes Deep Learning; phy730 Machine Learning; psy300 Statistical Learning; gsw550 Interdisciplinary Lecture Series Data Science and Data Ethics; inf820 Exploring Research Data Management; inf5402 Trustworthy Machine Learning; phy694 Machine Learning II; inf5400 Advanced Topics in Applied Deep Learning; mat940 Time Series Analysis; inf420 Introduction to IT-Security; inf1212 Designing Explainable Artificial Intelligence; inf5456 Applied AI: Multimodal-Multisensor Interfaces I: Foundations, User Modeling, and Common Modality Combination; inf5460 Applied AI: Multimodal-Multisensor Interfaces III: Language Processing, Software, Commercialization, and Emerging Directions; gsw555 Internship; gsw560 Current Topics in Data Science and Machine Learning; gsw565 Language Courses; gsw570 Study Abroad I: Data Science/Machine Learning; phy970 Theoretical Foundations of Machine Learning and Data Science; phy971 Group Project Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences; mat941 Mathematical Foundations of Statistical Learning; mat942 Introduction to numerical methods for partial differential equations; phy972 Computational Physics; mar753 Netzwerke und Komplexität; phy973 Current Topics in Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences; phy974 Information Processing and Communication; gsw571 Study Abroad II: Specialisation; inf525 Medical Data Pipelines; inf5406 Medical Data Analysis with Deep Learning; inf527 Big Data Analytics and Clinical Decision Support; gsw575 Group Project in Data Science in Medicine and Healthcare; inf589 Special Topics in "Medical Informatics" II; inf305 Medical Technology; inf524 Medical Basics; gsw220 Bioinformatics and Omics; gsw580 Current Topics in Data Science in Medicine and Healthcare; phy605 Digital Signal Processing; phy975 Hearing and Communication Acoustics; phy976 Algorithms for Speech Processing; phy977 Group Project Data-Driven Speech and Hearing Sciences; phy734 Introduction to Neurophysics; phy678 Processing and analysis of biomedical data; psy220 Human Computer Interaction; phy978 Current Topics in Data-Driven Speech and Hearing Sciences.

The principal rejection risk is a transcript that cannot support the programme's named academic foundation. Cost, housing, visa and employment are separate decisions after academic fit has been established.

Decision checkscomplete cost · entry fit · application timing · outcome evidence

Sources

These sources support the programme, admission, cost and immigration information used on this page.

Sources checked on September 4, 2026. The next intake covered here is Winter 2027/28.