Data Science and Machine Learning
Carl von Ossietzky University of Oldenburg
Program Overview
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.
EURFinancials
Financials
INFOIntakes and Duration
Intakes and Duration
Intake Season
Winter (October)
Duration
4 semesters
COCareer Outcomes
Career Outcomes
You can always explore various career paths after completing this program. The listed options are some of the most popular among graduates.
EURSalary Outlook
Salary Outlook
Typical range
€60,000 – €105,000 per year
Fresher, mid-level, and experienced ranges are linked to external sources so they stay auditable for applicants.
LANGLanguage & Exams
Language & Exams
Teaching Language
English
Applicants whose native language 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 six years old at most. 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 website of the university . 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.
REQRequirements
Requirements
Academic Admission
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. 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 six. 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 additional qualifications. Please include relevant documents in your application (e.g. employer's reference, internship certificate, supervisor's certificate). These are evaluated by the admissions committee using the criteria outlined below: 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) – one point per activity, max. two points in total. 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 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 the subject of the Bachelor's thesis We do not ask for letters of recommendation or letters of motivation!
APS Certificate
An APS certificate is required for applicants who have completed their qualifying education in India, China, or Vietnam.
IELTS Score
Not specified
Application Deadline
Application deadlines are subject to changes. Please refer to the above university course link for the most accurate information.
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