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To understand the challenges of data analysis
To be able to structure the information for an adapted analysis
To be able to choose an analysis methodology adapted to the case study
To be able to implement a professional analysis on concrete data sets
To be able to interpret, understand and produce statistical results
To understand the limits of these approaches, and consider alternatives, extensions, etc.
The course is structured around case studies to be treated according to a rigorous scientific approach and to discover different facets of data analysis in an industrial engineering context.
The course addresses different forms of data analysis:
Project : TP1
Oral presentation («soutenance») : S1
Exam : E1
Grade = 0.4*TP1 + 0.2*S1 + 0.4*E1
If oral presentations cannot be organized: Grade = 0.5*TP1 + 0.5*E1
Cette pondération est compatible avec une organisation des enseignements et des examens en distanciel
Une note de projet : TP1
Une note de soutenance : S1
Une note d'examen : E1
Note = 0.4*TP1 + 0.2*S1 + 0.4*E1
Lorsque les soutenances ne peuvent être faites dans le cours : Note = 0.5*TP1 + 0.5*E1
The course exists in the following branches:
Course ID : 4GUL10A5
Course language(s):
You can find this course among all other courses.
J.H. McDonald, (2009), Handbook of Biological Statistics, Sparky House Publishing.
I.H. Witten et E. Frank, (2005), DataMining – Practical machine learning tools and technics, Elsevier.
Stéphane Tufféry, (2005), Datamining et statistique Décisionnelle – L’intelligence dans les bases de données, Ed. Technip.
Cornillon et al., (2008), Statistiques avec R, Presses Universitaires de Rennes.
Gaël Millot, (2011), Comprendre et réaliser les tests statistiques à l'aide de R, 2ème édition, Editions De Boeck, 767 pages
Hill, Griffiths and Lim, (2011), Principles of Econometrics, Fourth Edition
Date of update June 5, 2015