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Algorithms for data analysis


Program (detailed contents):


The course is organized around four aspects of the data processing


1) Data structuration


In front of a mass of data, the first issue is to extract a structure. This requires knowledge in unsupervised learning and data mining, especially in clustering concepts. Classical algorithms related those knowledge are presented in this part of the course.


At the end of this module, the student will have understood and be able to explain (main concepts):

  • the different problems associated with data study
  • the main algorithms allowing to solve those problems
  • the principles of those algorithms and the main issues associated to their setting up
  • the main existing libraries


The student will be able to:

  • analyse the requirements of the data processing
  • set up the most efficient algorithms
  • use the algorithms that are implemented  in the main existing libraries
  • adapt and develop his/her own  algorithms
  • present and explain the results of those algorithms
  • program in Python and R languages

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...