# metadata.dc.description[].value
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Francisco Belchí Guillamón

# metadata.dc.description.abstract[].value
[en] Extracting information from data sets that are high-dimensional, incomplete and noisy is generally challenging. The aim of this work is to explain a homology theory for data sets, called Persistent Homology, and the topology and algebra behind it. Moreover, we will show different ways to represent it and finally computing some examples with the help of the GUDHI software for Python.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Fritz Pere Nobbe Fisas, 2020

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Matemàtiques