Sharpen your data skills – a part-time update, built around your job.
University Certificate
Data Science
Build the AI and data skills that matter on the job – coding, analysis, at your level.
Free Spots
9
Target Group
Working Professionals from all industries, Software & IT specialists (e.g., Developer, Data Engineer), Data Analysts, Business Analysts, Controllers, Consultants and product roles & Academics with a Master's or PhD
Deadlines
Application Deadline: September 15, 2026
Program Start: November 23, 2026
Study Fee
7.450 €
Concepts & Modules
The certificate program “Data Science” is aimed at professionals in the field of data analysis, computer science and statistics as well as professionals from all industries and fields in which large amounts of data are generated and are either already used or should be used. The program offers the opportunity to acquire and deepen practice-relevant knowledge and skills in the field of Data Science.The program consists of a total of four modules of the English-language, part-time master’s program “Data Science”, which is a cooperation between the University of Münster and the University of Twente (Netherlands). Participants can choose to combine the modules according to their individual preferences.The first basic module, “Introduction to Data Science and Programming Systems”, comprises seven days of attendance in Münster.
The other two modules of choice selected from the Master’s program have five attendance days each in Münster or Enschede. The fourth module consists of writing a project work and holding a presentation. Participants acquire a total of 32 ECTS credits in the program. The standard period of study is approximately 18 months, including the final examinations. In case of a subsequent entry into the Master’s program “Data Science”, crediting of the completed modules is possible. Profiles from all industries are welcome – prospective students should have a basic understanding of programming, statistics and mathematics.
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 20 – 24, 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
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
Lecturers & Scientific Management
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Find out what qualifications are required
Dates und Facts
Degree
University Certificate
Credit points
32 ECTS
Department
School of Business and Economics
Duration
approx. 12-14 months (depending on module choice)
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
- by arrangement as an individual payment plan
- exempt from VAT pursuant to § 4 No.21 a (bb) UStG
Target Audience
The Data Science Certificate is designed for professionals from all industries and fields who want to build practical, hands-on data skills – from a first step into Data Science to a targeted upskilling for those with an already advanced academic background, such as a Master’s or PhD. This includes professionals from:
- Software development and IT (e.g., Software Engineer, Developer, DevOps Engineer, Data Engineer) looking to add hands-on Data Engineering, MLOps, or Machine Learning skills to their existing role
- Data analysis, statistics, and computer science (e.g., Data Analyst, Data Scientist, BI/Analytics Engineer) who want to deepen and structure their practical expertise
- Business analytics and controlling (e.g., Controller, Business Analyst) moving from classic reporting into hands-on, data-driven work
- Consulting (e.g., Consultant), building practical data skills to strengthen data-based client work
- Finance and banking, upskilling in data analysis and AI-driven processes
- Healthcare, applying data analysis skills to diagnostics and process management
- Industry, manufacturing, and engineering (e.g., Cloud Engineer, Site Reliability Engineer), adding data skills for process optimization and quality assurance
- Natural sciences and research, gaining hands-on skills in modern data analysis
- Product management and sales (e.g., Product Manager, Product Owner, Key Account Manager, Customer Success Manager), applying AI tools and data analysis directly in day-to-day work
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
9
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