Data Science and Big Data

In this area of expertise we include the statistic methods and techniques to analyse and process data, as well as the integration of the necessary technology to be able to solve problems related to the treatment of big data volumes and their interoperability.

inLab specialises in:

  • Complex data treatment and analysis: data mining, advanced data and information analysis techniques, data fusion.
  • Big Data architectures (Open Source technologies)
  • Dashboards
  • Data warehouse
  • Statistical methods for the measurement of intangibles (satisfaction, image, etc.)

A lot of projects oriented to complex data analysis and treatment (e.g. data fusion, filtering and completion) or to Big Data technologies, have been developed within the framework of inLab FIB, as a result of which different entities and organisms have been able to develop tools and procedures that have sensibly improved their operative, generating valuable information for organisations.

Lastest posts related with this expertise

Distribució desl estudiants de la FIB per especialitats

The main purpose of data visualization is to communicate complex information or ideas in a clear, precise and efficient way, in a way that helps users to analyze and reason about data and evidence.

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Big Data

Since the creation of the Internet and increasingly, we have had data generated by many applications and we have within reach waiting to be used. Once, having and consuming all this enormous material could become a task virtually impossible or unfeasible both economically and technologically.

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Entrenador colocando el Viper Pod al chaleco del jugador.

If we pay attention at the workout images of the greatest football teams, we can see that the players wear a sort of black jacket. This has recently appeared, so you may not know what this has to do with our area. The elite clubs are fully getting into the technology world.

We all know that Big Data technology is being applied in many different areas.

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Projects related with this expertise

June, 2017 to October, 2017
big data image
May, 2016 to September, 2016
Termòmetre emocional
December, 2014 to December, 2015
January, 2015 present
November, 2017 present
November, 2016 to June, 2017
Pilot (week 1)
December, 2013 to June, 2015
Learning analytics : Learning from how we learn
January, 2013 to August, 2014
February, 2012 present
January, 2009 to December, 2012
Snow
February, 2008 to November, 2010
Improving Education

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