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INTERMEDIATE DATA ANALYSIS

2017-2018

IESEG School of Management ( IÉSEG )

Code Cours :

1718-IÉSEG-BA3S1-QMS-B3-CE02BE

QUANTITATIVE METHODS


Niveau Année de formation Période Langue d'enseignement 
Bachelor3S1English
Professeur(s) responsable(s)F.CHÂTEAU
Intervenant(s)Matthieu BUISINE, Frédéric CHATEAU


Pré requis

Students should be aware of some basic concepts in statistics (variance, cross tables, conditional probabilities), management (marketing) and micro-economy. They also should be informed with multivariate descriptive basic algorithms (PCA, linear model) or ideas.

Objectifs du cours

At the end of the course, the student should be able to:
- Build a data based predictive strategy, Formalize a scoring problem
- Carry out a research relying on discriminant analysis and decision trees.
- Evaluate the performance, control the reliability and accuracy of a score
This course aims at giving students a global contractor’s competence AND basic autonomy to address a scoring issue

Contenu du cours

Key words : Data Mining – Scoring – Big Data – Machine learning – Data Science
- Introduction to data based marketing, risk management and prediction techniques
- Introduction to scoring, ROI and simulation for targeted actions
- Discriminant analysis, Decision trees and Scores.
- Use of a statistical software: data management & statistical methods
- Interpretation of scores efficiency and reliability
- Presentation of alternative statistical or computing approaches: neural networks, k-NN.


Modalités d'enseignement

Organisation du cours

TypeNombre d'heuresRemarques
Face to face
Interactive class6,40  
Coaching4,00  
PBL class5,20  
Independent study
Group Project18,00   Project teams of 2 or 3 students
Individual Project2,00   4 practical sessions personal reports
Charge de travail globale de l'étudiant35,60  

Méthodes pédagogiques

  • E-learning
  • Project work
  • Interactive class
  • Case study
  • Coaching


Évaluation

Assessment mainly relies on students’ competences and ability to engage (and preferably achieve) a data based research. A short MCQ (1h) evaluates student’s knowledge derived from their data experiences. Individual assessments of practical sessions are bassed on students collaborative work (peer problem solving)

Type de ContrôleDuréeNombrePondération
Continuous assessment
Participation0,00425,00
Final Exam
Written exam1,00115,00
Others
Individual Project0,00160,00
TOTAL     100,00

Bibliographie

  • Stephane Tuffery : Data Mining and Statistics for Decision Making. John Wiley & Sons, 2011. -

  • Construction and assessment of classification rules. Douglas J. Hand - Wiley 1997 -

  • Slides of the course and students’ notes -


Ressources internet



 
* Informations non contractuelles et pouvant être soumises à modification
 
 
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