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INTERMEDIATE DATA ANALYSIS

2017-2018

IESEG School of Management ( IÉSEG )

Code Cours :

1718-IÉSEG-BA3S1-QMS-B3-CE03UE

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 have ideas on these topics.

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 some discriminant analysis methods 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 – carry out a data research and reports
- Interpretation of scores efficiency and reliability
- Alternative statistical or computing approaches: neural networks, k-NN, SVM, random forest.
- Introduction to Text Data, NLP, AI and Text Mining
- Loyalty, up-selling, risk (event, loss and premium), appetence, data strategy


Modalités d'enseignement

Organisation du cours

TypeNombre d'heuresRemarques
Face to face
Interactive class6,67  
Coaching4,00  
PBL class6,67  
Independent study
Group Project24,00   Project teams of 2 or 3 students
Individual Project5,67   4 practical sessions personal reports based on team work (peer learning in practical sessions)
Charge de travail globale de l'étudiant47,01  

Méthodes pédagogiques

  • Project work
  • Interactive class
  • Case study
  • Coaching
  • Pair work


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
MQC1,00115,00
Others
Group Project0,00160,00
TOTAL     100,00

Bibliographie

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

  • Provost & Foster: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking , O’Reilly Media, 2013 – an updated practical overview -

  • Hastie, Tibshirani & Friedman: The elements of statistical Learning, Springer Verlag, 2009 – a bible for next step -


Ressources internet



 
* Informations non contractuelles et pouvant être soumises à modification
 
 
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