Visualization techniques are able to capture aspects of the data that would not be easily observable with other analysis methods and are therefore great tools for data exploration.
Rita González Márquez
Rita is a PhD student in the Department of Data Science at the Hertie Institute for AI in Brain Health at the University of Tübingen and the IMPRS-IS graduate school. She is interested in using machine learning methods to explore datasets and uncover meaningful information. Particularly, she is working with unsupervised learning methods, with a focus on dimensionality reduction algorithms. In her research she uses neighbor embedding algorithms in combination with NLP techniques to produce meaningful two-dimensional visualization of textual data. Not only is she interested in using machine learning methods to analyze data, but also in developing new methods that preserve faithfully key aspects of the data structure.
Visualization techniques are able to capture aspects of the data that would not be easily observable with other analysis methods and are therefore great tools for data exploration.