Global Structure

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

Curriculum Overview

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 and Ethics Business Intelligence, Start-Up Incubator, External Lectures, Ethics, Responsibility, Data Privacy, etc.
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 Mahout, Weka, SciKit, RapidMiner, Spark / Data-Preparation, Data-Cleaning, Data-Integration
2 M11 Elective Module I Text Mining & NLP or ML as Service & Analytic Engines
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 Crash Course Business Administration, Analytics, Case Studies, Business Value, Business Cases / Plans, BMCs, etc.
3 M18 Elective Module II Deep Learning, Learning from Images, Sampling and Design, Learning and Intelligent Optimisation
3 19.1 Master Thesis  
4 19.2 Exam