# Visual Analytics ##### Summer ##### (in German: Visual Analytics )
##### **Module-ID:** **FIN-INF-120524**
**Abbreviation** **Credit Points** 6 **Semester** Summer **Term** starting at 1. **Duration** 1 Semester **Language** english **Level** Master
**Link:**[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20261&veranstaltung.dtxt=Visual+Analytics&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Visual Analytics")
**Limited accessability:**true
**Responsibility:**Bernhard Preim
**Lecturer:**Bernhard Preim
**Classes:**- Lecture *Visual Analytics* - Exercise class *Visual Analytics*
**Applicability in curriculum:**- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Visual Computing
**Intended learning outcomes:** Students ... - know machine learning methods and the visualization of their results - apply this knowledge to analyse financial and business data - use statistical methods to improve visual analytics solutions - can assess and compare clustering methods **Content:** - Introduction: Potential and applications of Visual Analytics - Visual Analytics based on clustering - Visual Analytics based on subspace clustering and biclustering - Visual Analytics with decision trees - Visual Analytics with association rules - Scatterplot-based visualizations - Visual Analytics of events sequences - Interaktive und Kooperative Methoden von Visual Analytics - Visual Analytics im Gesundheitswesen **Workload:** '- 56 contact hours + 124 h self study
**Pre-examination requirements:****Type of examination:****Teaching method / lecture hours per week (SWS):**
Written exam, 2 hours - 2 SWS Lecture - 2 SWS Exercise class
**Prerequisites according to examination regulations:****Recommended prerequisites:**
none - Lecture Visualization - Knowledge of Data Analysis, for example Intelligent Data Analysis - Data Mining - Machine Learning - Artificial Intelligence
**Media:****Literature:**
- Powerpoint presentation - Whiteboard - Videos - J. J. Thomas, K. A. Cook (Hrsg.): Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society 2005 - D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, H. Ziegler: Visual analytics: Scope and challenges. Visual Data Mining, 2008 - Andrienko, N., Andrienko, G., Fuchs, G., Slingsby, A., Turkay, C., & Wrobel, S. (2020). *Visual analytics for data scientists*. Springer Nature. - Kohlhammer, J., Proff, D. U., & Wiener, A. (2018). *Visual business analytics: effektiver Zugang zu Daten und Informationen*. dpunkt. verlag.
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