- CMI - CURSUS DE MASTER EN INGENIERIE

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SAS Entreprise Miner

Volume horaire

Unité d’Enseignement

Semestre

Niveau

Cours

TD

15

0

Complémentaire

9

M2 ISF

Enseignante

Evaluation

Coefficient

ECTS

Richard Eudes

Projet

1

1.25


 
 


Objectifs de l’enseignement
This course covers the skills required for a data miner / data scientist to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).
This course can help prepare you for the following certification exam(s): Predictive Modeling with SAS Enterprise Miner (if students are motivated by this).

Descriptif de l’enseignement

- Assessing and assaying prepared data
- Predictive modeling: Decision trees
- Predictive modeling: regressions
- Predictive modeling: Neural networks
- Model assessment
- Model implementation
- Pattern discovery
- Special topics

Méthode d’enseignement

Computer-assisted teaching

Pré-requis

Before attending this course, you should have at least an introductory-level familiarity with statistics and optimization (operations research). Previous SAS software experience is helpful (Base SAS programming is a plus).

Bibliographie (ouvrages uniquement)

  • SAS Institute Inc. 2002. SAS® 9 Language: Reference, Volumes 1 and 2. Cary, NC: SAS Institute Inc.

  • SAS Institute Inc. 2002. SAS® 9 Procedures Guide. Cary, NC: SAS Institute Inc.

  • SAS Institute Inc. 2002. SAS/STAT® 9 User's Guide, Volumes 1,2, and 3. Cary, NC: SAS Institute Inc.



 
 
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