Data Science

Data Science is an interdisciplinary field that studies where data (structured or unstructured) comes from, what it represents, and how to extract the knowledge contained within it to assist in decision-making processes. It uses concepts and algorithms from areas such as statistics, artificial intelligence, machine learning, and data mining to solve complex problems. Data Science is seen as a process that encompasses several stages: problem definition, data collection, data preparation, data preprocessing, knowledge extraction algorithm selection and parameter tuning, model training and validation, as well as continuous evaluation of the overall process.

Faculty:

Alceu de Souza Britto Jr.

  • PhD, Pontifical Catholic University of Paraná, 2001.
  • CNPq Research Productivity Researcher.
  • Image Processing, Pattern Recognition.

 

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Carlos Nascimento Silla Junior

  • PhD, University of Kent, Inglaterra, 2011.
  • CNPq Research Productivity Researcher.
  • Member of ACM, IEEE, ISMIR, SBC.
  • Data Mining, Computational Intelligence, Music Information Retrieval, Computer Music Technology.

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Fabrício Enembreck

  • PhD, Université de Technologie de Compiègne, France, 2003.
  • CNPq Research Productivity Researcher.
  • Multi-Agent Systems, Adaptive Agents, Data Mining.

 

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Jean Paul Barddal

  • PhD, Pontifical Catholic University of Paraná, 2018.
  • Data Mining and Machine Learning on Data Streams, Classification, Regression, Concept Change, Attribute Selection and Clustering.

 

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Júlio César Nievola

  • PhD, Universidade Federal de Santa Catarina, Brazil, 1995.
  • Member of IEEE, ACM.
  • Data Mining, Intelligent Systems, Artificial Neural Networks.

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Vinícius Mourão Alves de Souza

  • PhD, Universidade de São Paulo, Brasil, 2016
  • Data Mining, Machine Learning, Data Stream, Time Series Mining, Smart Sensors.

 

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