TU Wien:Masterstudium Data Science
| Links | Ehemalige LVAs, Studienplan |
|---|---|
| VoWi-Stats |
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LVAs nach Pflichtmodulen[edit]
No value
BDHPC/CO - Big Data and High Performance Computing - Core
BDHPC/FD - Big Data and High Performance Computing - Foundations
DSA - Domain-Specific Aspects of Data Science
- Data Retrieval in Earth Observation VO (Wagner)
- Interdisciplinary Lecture Series for on Data Science VU (Hanbury)
- Interdisciplinary Project in Data Science PR (Rauber, Hanbury)
- Introduction to Earth Observation VO (Wagner)
FDS/CO - Fundamentals of Data Science - Core
FDS/FD - Fundamentals of Data Science - Foundations
- AKSTA Statistical Computing VU (Filzmoser)
- Datenorientierte Programmierparadigmen VU (Hanbury)
- Experiment Design for Data Science VU (Knees)
MLS/CO - Machine Learning and Statistics - Core
MLS/FD - Machine Learning and Statistics - Foundations
- Advanced Methods for Regression and Classification VU (Filzmoser)
- Machine Learning VU (Mayer, Musliu)
VAST/FD - Visual Analytics and Semantic Technologies - Foundations
LVAs nach Wahlmodulen[edit]
BDHPC/EX - Big Data and High Performance Computing - Extension
- Algorithmics VU (Raidl)
- Analysis 2 VO (Müllner)
- Datenbanktheorie VU (Pichler)
- Effiziente Programme VU (Ertl)
- GPU Architectures and Programming VU (Bartocci)
- Graph Drawing Algorithms VU (Nöllenburg)
- Heuristic Optimization Techniques VU (Raidl)
- High Performance Computing VU (Träff)
- Structural Decompositions and Algorithms VU (Slivovsky)
FDS/EX - Fundamentals of Data Science - Extension
- Data Stewardship UE (Rauber)
- Digital Humanism VU (Werthner)
- Organizational Aspects of IT-Security VU (Weippl)
- Systems and Applications Security VU (Lindorfer)
- User Research Methods PR (Fitzpatrick)
- User Research Methods VU (Frauenberger)
Fachübergreifende Qualifikationen
- Didaktik in der Informatik (Abenteuer Informatik) SE (Futschek)
- Didaktik in der Informatik SE (Weissenböck)
- Kommunikation und Moderation VU (Pohl)
- Kommunikation und Rhetorik 2 SE (Pichlmair, Riedler)
Freie Wahlfächer - Informatik
Freie Wahlfächer und Transferable Skills
MLS/CO - Machine Learning and Statistics - Core
MLS/EX - Machine Learning and Statistics - Extension
- Business Intelligence VU (Tjoa)
- Intelligent Audio and Music Analysis VU (Knees)
- Modeling and Simulation VU (Bicher)
- Multivariate Statistik VO (Filzmoser)
- Problem Solving and Search in AI VU (Musliu)
- Selbstorganisierende Systeme VU (Rauber)
- Similarity Modeling 2 VU (Eidenberger)
- Social Network Analysis VU (Neidhardt)
VAST/CO - Visual Analytics and Semantic Technologies - Core
VAST/EX - Visual Analytics and Semantic Technologies - Extension
- Deductive Databases VO (Simkus)
- Description Logics and Ontologies VU (Ortiz de la Fuente)
- Echtzeit-Visualisierung VU (Purgathofer)
- Informationsvisualisierung UE (Waldner)
- Knowledge-based Systems VU (Egly, Eiter, Tompits)
- Natural Language Processing and Information Extraction VU (Hanbury)
- Semantic Web Technologies VU (Ortiz de la Fuente)
- Semi-Automatic Information and Knowledge Systems VU (Sabou, Lanzenberger)
- Verarbeitung deklarativen Wissens VO (Egly)
- Visual Data Science VU (Schmidt)
- Visualisierung 2 VU (Gröller)
VAST/FD - Visual Analytics and Semantic Technologies - Foundations
- Einführung in Semantic Systems VU (Tjoa)
- Information Visualization UE (Gschwandtner)
- Information Visualization VO (Gschwandtner, Aigner, Miksch)
- Informationsvisualisierung VO (Matkovic)
Wahlfächer