Start Your Studies in 2026: Application Phase Open Now!

Data Science (M.Sc.)

Integrate AI Excellence: Explore Big Data's Potential with Our Part-Time Program and Elevate Your Career in the Tech World

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Target group

Working professionals from the fields of e-commerce, logistics, social media, consulting, analysis, and any field that benefits from the usage of big data

Deadlines

Application Deadline: September 15, 2026
Program Start: November 23, 2026

Study Fee

16.950 €

Your Masters Degree in Data Science

Our English-speaking interdisciplinary and international Data Science Master program is designed for full-time working professionals and equips you with the essential skills to tackle the challenges posed by the ever-growing volume, velocity, and variety of data, commonly known as ‘big data‘. Alongside a strong foundation in programming, data analysis, data management, and AI, the program especially focuses on hands-on application within real-world business settings. Participants will learn about cutting edge developments in AI and Deep Learning (e.g., Large Language Models).

Lecture & Modules

The standard period of study for the Master’s program in Data Science is 4 semesters. During this time, you will complete 9 modules, including a project work and the Master’s thesis. An optional preparatory course in e-learning format is also offered. The program is designed for high compatibility with your profession. Modules are typically conducted in blocks of five consecutive days (Monday to Friday). On average, a module takes place every 6 to 12 weeks.

The lectures are held in groups with a maximum of 25 participants, spanning a total of 37 days of in-person attendance. In addition to its interdisciplinary approach, the uniqueness of the program lies in its international collaboration between the University of Münster and the University of Twente, offering courses in English. This part-time education leads to a full Master of Science degree from the University of Münster.

Introduction to Data Science and Programming Systems

November 23 – November 29, 2026

  • Introduction to data science and its applications.
  • Overview of programming systems: R and Python
  • Fundamentals of Python programming: variables, control structures, data structures, etc.
  • Introduction to R programming: data structures, functions, loops, etc.
  • Exploratory data analysis using R.

Prof. Dr. Heike Trautmann
Prof. Dr. Gottfried Vossen

Data Management

February 15 – February 19, 2027

  • Data management using SQL, NoSQL, or vector databases
  • Working with MySQL and MongoDB in Python
  • Data mining using Apriori implementations
  • Sentiment analysis
  • Dimensionality reduction
  • Building an LLM from scratch using PyTorch
  • Text preparation, attention mechanism, pretraining, and fine-tuning
  • An LLM for spam classification and as a chatbot

Prof. Dr. Gottfried Vossen

Data Analytics

June 7 – June 11, 2027

  • Exploratory data analysis and data preprocessing
  • supervised learning (classification, regression)
  • unsupervised learning (cluster analysis, dimensional reduction)
  • model validation
  • Programming in R

Prof. Dr. Heike Trautmann
Prof. Dr. Pascal Kerschke

Social Media & Communications

September 2027

  • Social research using big data and online sources
  • Basics of online communication and psychology
  • Network analysis using R, including base concepts, matrix calculations and (sub)structure detection
  • Computation content analysis using R, including Natural Language Processing, Sentiment Analysis, Topic Modeling
  • Advanced Methods of text analysis (preview)

Prof. Dr. Thorsten Quandt

IT-Management, IT-Security, Ethics, Legal Aspects

November 2027

  • Management challenges of growing data volumes
  • IT security considerations in data management
  • Ethical aspects in IT management and data handling
  • Legal framework and regulations for data management
  • Utilizing open data for generating value

Prof. Dr. Jos van Hillegersberg

Self-Management & Leadership

February 2028

  • Influence of data science results on management decisions
  • Data science as part of decision-making processes
  • Behavior of executives and the special role of data scientists
  • Quality of innovative teams and their effects on data science results
  • Methods for effective negotiations
  • Methods for presenting and discussing data science results with decision makers

Dr. Michael Ehrenhard

Practical Phase & Project Work

Kick-Off: September/October 2027
Presentations: January 2028

  • Case Analysis in Data Science
  • Creative problem-solving of complex real-life cases
  • Practical implementation of chosen solutions
  • Peer feedback and reflection
  • Presentation Skills

Dr. Niels Pulles

Application Areas

May 2028

  • Hands-on workshop: Advanced methods of text analysis using AI (including LLMs / transformer models)
  • Data-driven decision making
  • Application of learned techniques marketing, customer relationship management, supply chain management, logistics, start-ups, etc.
  • Future developments: critical discussions about the future of data-driven decisions, including the potential for automated decisions by machines
  • Interdisciplinary approach

Prof. Dr. Thorsten Wiesel

Lecturer & Academic Director

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Information Material

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Admission Requirements

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Dates and Facts

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Degree

Master of Science

Credit points

90 ECTS

Department

School of Business and Economics

Duration

4 Semesters

Start Date

November 23, 2026, First on-site Module

Application Deadline

September 15, 2026

Language of Instruction

English

Location(s) of Instruction

Münster, Germany | Enschede, Netherlands

Participation Fee

16.950 € in three instalments

  • an individual payment plan may be arranged
  • exempt from VAT pursuant to § 4 No.21 a (bb) UStG

Target Audience

Working professionals from the fields of e-commerce, logistics, social media, consulting, analysis, and any field that benefits from the usage of big data

Admission Requierments

  • Completion of a first academic degree
  • Professional working experience of at least one year
  • Evidence of English language proficiency at level B2 (TOEFL, IELTS, etc.)

Free spots

25

Do you want a knowledge update on AI and Data Essentials

Customize your focus by selecting modules from the Data Science master’s program and design your University Certificate.

University Certificate Data Science

Further Topics

Your time in Münster

A fifth of Münster's 300,000 inhabitants are students at one of its eight universities, of which the University of Münster is by far the largest. Students shape life and culture in Münster, creating a diverse and vibrant city.

Find out more

In Cooperation with the University of Twente

"The University of Twente is a smart living lab where talented students and stuff provide groundbreaking research, exiting innovations and inspiring education."
Therefore we are happy to have Twente as a strong educational partner.

Find out more

Certificate in Data Science

The certificate provides the knowledge and practical skills necessary for data managers to analyze "Big Data". It comprises four modules (2 elective & 2 compulsory) from the English-language, part-time master's program.

Find out more

We are happy to help you with your questions

Jannis Wegmann

Student advice and support

Mo–Fr: 9.00 am - 4.00 pm

+49 251 83 27100
jannis.wegmann@uni-muenster.de