Data Analysis for Industrial Engineering - 4GUL10A5

A+Augmenter la taille du texteA-Réduire la taille du texteImprimer le documentEnvoyer cette page par mail

Number of hours
- Lectures : 15.0
- Tutorials : 15.0
- Laboratory works : -
- Projects : -
- Internship : -
- Written tests : 2.0
ECTS : 3.0
Officials : Pierre LEMAIRE, Iragael JOLY

Goals

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.

Content

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:

Data mining (e.g., analysis of variance, principal component analysis, etc.)
Data segmentation (e.g. clustering, decision rules, etc.)
Supervised learning (e.g. regression, classification, survival analysis...)

In doing so, we will develop different aspects that are essential for a good analysis:
structuration and manipulation of the information contained in multidimensional data for an adapted analysis, including the management of errors and other missing data.
validation of the results obtained: validation method, indicators used, interpretation of results
understanding the limitations of these approaches and their alternatives

Most of this course will be done through practical exercises and case studies using R/Rstudio software.

Prerequisites

Statistics (descriptive and summary statistics; estimation by moment method and maximum likelihood, confidence interval; test of mean and proportion)
Manipulation de la donnée (2A)
Introduction to R soktware

Tests

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

Calendar

The course exists in the following branches:

Curriculum - Engineer student Master SCM - Semester 8
Curriculum - Engineer student Master PD - Semester 8

see the course schedule for 2022-2023

Additional Information

Course ID : 4GUL10A5
Course language(s):

You can find this course among all other courses.

Bibliography

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

A+Augmenter la taille du texteA-Réduire la taille du texteImprimer le documentEnvoyer cette page par mail

Date of update June 5, 2015