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curriculum

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about the curriculum

  • 120 ECTS in total
  • lehrveranstaltung $\in$ modul $\in$ prüfungsfach
  • a "modul" can either be a "pflichtmodul" (mandatory) or a "wahlmodul" (elective)
  • wahlmodul:
    • courses must be chosen from "schlüsselbereiche SB" (specialization areas)
    • each specialization can have 6-24 ECTS
    • you must pick at least 2 specializations, each made of a "core" and an "extension" part:
      • the "core" part has 2 courses (6 ECTS in total) that you must do together, as a prerequisite for the "extension" part
      • the "extension" part has an arbitrary number of courses, that you're allowed to do 18 ECTS from at most
  • once you're finished with all courses + your thesis + the final seminar presentation of your thesis you're eligible for the defense

courses

ECTS
Semester 1
VU Data-oriented Programming Paradigms (fds/fd) 3.0
VU Experiment Design for Data Science (fds/fd) 3.0
VU Advanced Methods for Regression and Classification (mls/fd) 4.5
VU Machine Learning (mls/fd) 4.5
VU Semantic Systems (vast/fd) 3.0
VU Interdisciplinary Lecture Series on Data Science (dsa) 1.0
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Σ 19.0
Semester 2
VU Statistical Computing (fds/fd) 3.0
VU Advanced Database Systems (bdhpc/fd) 6.0
VU Data-intensive Computing (bdhpc/fd) 3.0
VO Information Visualization (vast/fd) 3.0
VO Cognitive Foundations of Visualization (vast/fd) 3.0
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Σ 18.0
Semester 3 (Project)
PR Interdisciplinary Project in Data Science (dsa) 5.0
VU Domain-Specific Lectures in Data Science (dsa) [electives] 3.0
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Σ 8.0
Electives (at least 2 modules)
(a) FDS module
├── VU Data Acquisition and Survey Methods (core) 3.0
├── VO Data Stewardship (core) 3.0
└── ... extensions ≤18.0
(b) MLS module
├── VU Recommender Systems (core) 3.0
├── VU Statistical Simulation and Computer Intensive Methods (core) 3.0
└── ... extensions ≤18.0
(c) BDHPC module
├── VU Basics of Parallel Computing (core) 3.0
├── VU Efficient Programs (core) 3.0
└── ... extensions ≤18.0
(d) VAST module
├── VU Advanced Information Retrieval (core) 3.0
├── UE Design and Evaluation of Visualisations (core) 3.0
└── ... extensions ≤18.0
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Σ 36.0
Free electives, transferable skills
… pick from course catalogue 9.0
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Σ 9.0
Thesis
SE Seminar for Master Students in Data Science (multiple terms) 1.5
Thesis, Diploma thesis 27.0
Final exam / Defense, Final board exam 1.5
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Σ 30.0

electives

ECTS
FDS module
VU Advanced Cryptography 6.0
VU Communicating Data 3.0
VU Data Center Operations 3.0
UE Data Stewardship 3.0
VU Computational Social Science 3.0
VU Digital Humanism 3.0
VU Internet Security 3.0
VU Organizational Aspects of IT-Security 3.0
VU Software Security 3.0
VU Sustainability in Computer Science 3.0
VU Systems and Applications Security 6.0
VU User Research Methods 3.0
PR User Research Methods 3.0
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––
MLS module
VU Advanced Learning Methods 3.0
VU Advanced Modeling and Simulation 3.0
VU Advanced Reinforcement Learning 3.0
VU AI/ML in the Era of Climate Change 4.0
VU AKNUM Reinforcement Learning 6.0
VU Algorithmic Social Choice 6.0
VU Applied Deep Learning 3.0
VO Bayesian Statistics 3.0
UE Bayesian Statistics 2.0
VU Bayesian Statistics 5.0
VU Business Intelligence 6.0
VU Crypto Asset Analytics 3.0
VU Deep Learning for Visual Computing 3.0
VU General Regression Models 5.0
VO General Regression Models 3.0
UE General Regression Models 2.0
VU Generative AI 3.0
VU Intelligent Audio and Music Analysis 4.5
VO Introduction to Statistical Inference 4.5
UE Introduction to Statistical Inference 2.0
VU Machine Learning for Visual Computing 4.5
VU Mathematical Programming 3.0
VU Modeling and Simulation 3.0
VU Modelling and Simulation in Health Technology Assessment 3.0
VO Multivariate Statistics 4.5
UE Multivariate Statistics 1.5
VU Probabilistic Programming and AI 6.0
VU Problem Solving and Search in Artificial Intelligence 3.0
VU Security, Privacy and Explainability in Machine Learning 3.0
VU Self-Organizing Systems 4.5
VU Similarity Modeling 1 3.0
VU Similarity Modeling 2 3.0
VU Social Network Analysis 3.0
VU Theoretical Foundations and Research Topics in Machine Learning 3.0
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––
BDHPC module
VU Algorithmic Geometry 4.5
VU Algorithmics 6.0
VO Analysis 2 3.0
UE Analysis 2 4.5
VU Approximation Algorithms 3.0
VU Complexity Analysis 3.0
VU Database Theory 3.0
VU Fixed-Parameter Algorithms and Complexity 4.5
VU Frontiers of Algorithms and Complexity 3.0
VU GPU Architectures and Computing 6.0
VU Graph Drawing Algorithms 4.5
VU Hands-On Cloud Native 6.0
VU Heuristic Optimization Techniques 4.5
VU High Performance Computing 4.5
VO Nonlinear Optimization 3.0
UE Nonlinear Optimization 2.0
VU Optimization in Transport and Logistics 3.0
VU Structural Decompositions and Algorithms 3.0
VU Advanced Multiprocessor Programming 4.5
VU Randomized Algorithms 3.0
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––
VAST module
VO Deductive Databases 3.0
VU Description Logics and Ontologies 3.0
VU Document Analysis 3.0
UE Information Visualization 1.5
VU KBS for Business Informatics 6.0
VU Knowledge-based Systems 6.0
VU Knowledge Graphs 3.0
VO Medical Image Processing 3.0
UE Medical Image Processing 3.0
VU Natural Language Processing and Information Extraction 3.0
VO Processing of Declarative Knowledge 3.0
VU Research Topics in Natural Language Processing 3.0
VU Real-time Visualization 3.0
VU Semantic Technologies 3.0
VU Semi-Automatic Information and Knowledge Systems 3.0
VU Visual Data Science 3.0
VU Visualization 2 4.5