Datenanalyse, Visualisierung und Visual Analytics

Winter

(engl. Data Analysis, Visualization and Visual Analytics )

Angestrebte Lernergebnisse:
The students ...

• know methods of data analysis, visualization and visual analytics, including algorithmic methods for data preparation, dimension reduction and cluster analysis.
• know visualization techniques for the representation of high-dimensional data and interactive visual analytics approaches for decision support.
• know cognitive and perceptual psychological aspects of data visualization as well as principles of effective graphical representations.
• are able to select, apply and interpret suitable methods and tools for the analysis and visualization of complex data sets.
• are able to technically implement and evaluate visualization and analysis models and integrate them into suitable applications.
• are able to critically evaluate and optimize the quality and informative value of data visualizations and analytical models.
• are able to independently develop and present data-based solutions for simple problems from the application.

Inhalt:
Contents of the course:

• Automated data analysis: Introduction to machine learning and statistical methods for data analysis and their mathematical foundations (especially in multivariate optimization). Techniques covered include PCA for dimension reduction, k-means for clustering and regression analysis for modeling correlations and much more.
• Data visualization: Methods for graphical representation of data to identify patterns and trends, including scatterplots, heatmaps and parallel coordinates.
• Visual analytics: Combination of data mining methods and interactive visualizations for the analysis of complex data sets. Use of dashboards and reporting tools to support decision-making.
• Interdisciplinary aspects: Influence of visual perception and cognitive processing on effective visualizations. Consideration of color theory, design principles and usability.
• Application examples: Practical applications from financial data analysis, bioinformatics and epidemiology, for example for disease monitoring or gene expression analysis.
• The course combines theory and practical application scenarios to prepare students for independent data analysis and visualization tasks.

Arbeitsaufwand:
30 h attendance time + 120 h independent work

Hinweise: