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Master Data Science: Unterschied zwischen den Versionen
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The goal of ths page is to present a few possible "Tracks" through the Master Programme Data Science. | The goal of ths page is to present a few possible "Tracks" through the Master Programme Data Science. | ||
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* Visual Data Science VU (J. Schmidt) (VAST/EX) | * Visual Data Science VU (J. Schmidt) (VAST/EX) | ||
This class nicely complements classes "Gestaltung und Evaluation von Visualisierungen UE" and " | This class nicely complements classes "Gestaltung und Evaluation von Visualisierungen UE" and "Informationsvisualisierung VO". | ||
The latter give foundations on how to visualize data, and how to evaluate visualizations. | The latter give foundations on how to visualize data, and how to evaluate visualizations. | ||
The class Visual DS is a little more hands on, where in exercises data sets are analyzed visually and statistically, and there is the choice of creating a dashboard for a concrete dataset. | The class Visual DS is a little more hands on, where in exercises data sets are analyzed visually and statistically, and there is the choice of creating a dashboard for a concrete dataset. | ||
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* User Research Methoden VU (G. Fitzgerald) (FDS/EX) | * User Research Methoden VU (G. Fitzgerald) (FDS/EX) | ||
This class complements "Data Acquisition and Survey Methods". While the latter almost exclusively focuses on quantitative methods for analysing data from experiments, User Research almost exclusively focuses on qualitative methods. It could be beneficial what kind of data such methods produce, and how they can be analyzed with data science approaches, and what tools can help with analyzing such data. Additionally, there are often mixed | This class complements "Data Acquisition and Survey Methods". While the latter almost exclusively focuses on quantitative methods for analysing data from experiments, User Research almost exclusively focuses on qualitative methods. It could be beneficial to understand what kind of data such methods produce, and how they can be analyzed with data science approaches, and what tools can help with analyzing such data. Additionally, there are often mixed method studies that employ quantitative as well as qualitative methods, and understanding the latter is then necessary to fully grasp such studies. | ||
== Track "High-Performance Computing, Algorithms, Optimization" == | == Track "High-Performance Computing, Algorithms, Optimization" == | ||
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[[Kategorie: | [[Kategorie:Studium]][[Kategorie:English]] | ||