=====Course unit: Data and Decisions =====
==== Course metadata ====
  * Title in French: Données et Décisions
  * Course code: tba
  * ECTS credits: 8
  * 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): François Brucker, Michaël Chalamel (L'Oréal), Franck Chevalier (EY), Emmanuel Daucé, Christophe Pouet
  * //Last update 04/07/2022 by C. Pouet//

==== Brief description ====

This course unit is divided into four parts:
  * ** Statistical learning ** (24 hours) taught by Christophe Pouet.
  * ** Python for data science ** (24 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 tba.

==== Learning outcomes ====

  * 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

==== Course content ====
=== Statistical learning===
   - Introduction
     - Classical problems: regression, classification
     - Supervised, unsupervised and semi-supervised learning
     - Curse of dimensionality
   - Regression
     - Multiple linear regression, OLS method
     - Shrinkage-type methods (LASSO, Ridge)
     - k-nearest neighbors
   - Classification
     - Logistic regression
     - k-nearest neighbors
     - SVM
     - Rosenblatt perceptron and neuronal networks
=== Python for data science===
   - Dataframe: data exploration and data description
   - Spotting patterns using factor
     - Principal Component Analysis
     - Correspondence analysis
   - Prediction using trend analysis
     - Linear regression
     - Logistic regression
   - Data classification
     - Classification using partitions
     - Hierarchical methods
=== Data-driven decision making===
   - What is data?
   - How do we take decision?
   - Data governance and data quality
   - How to develop data-based decision making?
   - Data platform and data architecture
=== Data Project: data sources and preprocessing ===
tba

==== Bibliography ====
You can check the availability of the books below at [[https://documentation.centrale-marseille.fr/|Centrale Marseille library]].
  - 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.
  - Python for data science
     * Jannach, D., Zanker, M., Felfernig, A. and Friedrich, G. (2010). Recommender Systems: An Introduction. Cambridge. 
  - Advising using data
     * tba