• Title in French: Données et Décisions
• Course code: tba
• ECTS credits: 4
• Type: advanced course
• Semester 9 (Fall-Winter)
• Teaching period: Mid-November to Mid-February
• Teaching hours: 100h
• Language of instruction: French
• Coordinator: tba
• Instructor(s): Marie Billaud-Friess, Michaël Chalamel (L'Oréal), Franck Chevalier (EY), Emmanuel Daucé, Christophe Pouet, , Sitraka Forler (Post Luxembourg), Lirone Samoun (smartpush)
• Last update 02/09/2025 by C. Pouet

This course unit is divided into four parts:

• Statistical learning (30 hours) taught by Christophe Pouet.
• Python for data science (18 hours) taught by François Brucker and Emmanuel Daucé.
• Advising using data (24 hours) taught by Michaël Chalamel and Franck Chevalier.
• Data Project: data sources and preprocessing (24 hours) taught by Sitraka Forler and Lirone Samoun.

• Know how to model and program an estimation problem
• Know how to model and program a classification problem
• Know how to acquire and aggregate data
• Know how to use data to take decisions
• Understand the importance of data governance and data quality

1. Introduction
   1. Classical problems: regression, classification
   2. Supervised, unsupervised and semi-supervised learning
   3. Curse of dimensionality
2. Regression
   1. Multiple linear regression, OLS method
   2. Shrinkage-type methods (LASSO, Ridge)
   3. k-nearest neighbors
3. Classification
   1. Logistic regression
   2. k-nearest neighbors
   3. SVM
   4. Rosenblatt perceptron and neuronal networks

1. Dataframe: data exploration and data description
2. Recommendation systems (including KNN, PCA and SVD)
3. Data visualization (including maps, geopandas, …)

1. What is data?
2. How do we take decision?
3. Data governance and data quality
4. How to develop data-based decision making?
5. Data platform and data architecture

tba

You can check the availability of the books below at Centrale Méditerranée library.

1. Statistical Learning
   • James G., Witten D., Hastie T. and al. (2013). An introduction to statistical learning: with applications in R. New York: Springer
   • Hastie T., Tibshirani R. and Friedman J. (2013). The elements of statistical learning: data mining, inference, and prediction. New York: Springer.
   • Cornillon P-A., Matzner-Løber E. et al. (2010). Régression avec R. Paris: Springer.
2. Python for data science
   • Jannach, D., Zanker, M., Felfernig, A. and Friedrich, G. (2010). Recommender Systems: An Introduction. Cambridge.
3. Advising using data
   • tba