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DU Big data et statistique pour l'ingénieur

  • Organisation: ENSC - Bordeaux INP
  • Type of Course: Initial education - Life-long learning
  • Language(s): French
  • Place: ENSC Bordeaux (French Regions: Nouvelle-Aquitaine)
  • Prepared diploma/grade/title: DU or DE: Specific French University/Higher Education Institute one-year diploma (other acronyms: DES, DSP, DESA, DESU, DESUT)
  • Level of entry: French Baccalaureat + 5

Course Details

Objectives: This training aims, at the end of the course:
- to make training trainees aware of the current and future issues and tools of Big Data and Artificial Intelligence (AI);
- to master the tools of Statistics, data processing and AI, and to implement them in concrete applications.

The theoretical presentation of the different methodologies of Statistics and AI will be illustrated by many practical cases. Current Big Data tools and their implementations will be presented by start-ups specializing in the field, hosted at ENSC. The mastery of many methods will be offered using free software R intended for Statistics and Data Science, software allowing participants to possibly continue their activities with this same tool. Other software (Python, Matlab) and digital tools (IBM Watson, etc.) will also be used during the training.

Degree Level (EU) : (niveau 7) - (EQC level or equivalent)

Admission requirements: The training is open with the authorization of the school director; after opinion of the educational committee and after examination of an application file specifying the diplomas, professional level, experience of each candidate, and including a statement of reasons. The file may possibly be accompanied by letters of support from company officials, or other people such as teachers or researchers with whom the candidate has made initial contacts.

Duration and terms: 144 hours of training from January to July (6 to 7 sessions of 3 days 1 to 2 times per month). Possible internship during the training.

Dedicated web site: https://ensc.bordeaux-inp.fr/fr/big-data-et-statistique-pour-l-ingenieur

Disciplines

  • Engineering Sciences

Topics

  • 11 - Computer Science, Applied Mathematics, Modelling, Optimisation, Digital and Data Sciences, Big Data, Cryptography, Cybersecurity, Artificial Intelligence