This Master consists of 5 building blocks focusing on the following areas:

  1. Statistic Basics
  2. Computer Science Basics
  3. Machine Learning
  4. Practical Projects
  5. Data Science and Business Value & Ethics
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Term Nr. TITLE CONTENT  
1 M01 Mathematical Models Algebra, Vectors, Matrices, Probability, etc.  
1 M02 Advanced Softwareengineering Clean Code, Delivery Engineering, Functional Concepts, Architectures, etc.  
1 M03 Statistical Computing Basics, implementations in R, Descriptive Statistics, etc.  
1 M04 Practical Data Science Programming Python Basics, NumPy, Pandas, Toolsets bindings, etc.  
1 M05 Computer Science for Big Data Big Data Architecture, Cloud Management, Docker, Streaming, NoSQL, Search, etc.  
1 M06 Business Intelligence und Data Science Plattformen Business Intelligence, Start-Up Incubator, Data Science Platforms and Workbenches  
         
2 M07 Data Visualization Visualization Principles, Visualization for Different Datasets  
2 M08 Regression Multiple Linear Regression Models, Generalised Linear Regression Models, Survival Times as Target Values, Censoring, Log-Rank Tests, Cox-Regression, etc.  
2 M09 Machine Learning I Supervised / Unsupervised Learning, Associations Analytics, Clustering, Classification, Discriminant Analysis, Decision-Trees, etc.  
2 M10 Applications 1: Data Science Workflow / Applications Data-Preparation, Data-Cleaning, Data-Integration  
2 M11 Elective Module I Text Mining & NLP or Deep Learning  
         
3 M12 Machine Learning II Consolidation ML I, Resampling and Ensembles, Prediction Models, Bayes Networks, R in Practice  
3 M13 Application 2: Urbane Techn Data and Problem Fields in UT Areas, IoT, Applications: Energy, Smart Home, IoT, Traffic Data, etc.  
3 M14 Application 3: Enterprise Data Science Added Value Analytics, Text Mining, Relation Extraction, Prediction Models, Capstone Project as Start-Up Incubator  
3 M15 Studium Generale Politics- and Social Sciences, Human Sciences, Business-, Law- and Human Sciences, Languages  
3 M16 Studium Generale (same course)  
3 M17 Business Value and Responsibility Business Value und Verantwortung Analyse, Fallstudien, Business Value, Business Cases / Plans, Ethics, Responsibility, Data Protection
3 M18 Elective Module II Learning from Images, Learning and Intelligent Optimisation, Advances in KI Research  
         
3 19.1 Master Thesis    
4 19.2 Exam