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en:ddefidode [2021/03/23 12:23] cpoueten:ddefidode [2021/04/23 18:57] (Version actuelle) – [Course content] rbourles
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 +=====Course unit: Data and Decisions =====
 +==== Course metadata ====
 +  * Title in French: Données et Décisions
 +  * Course code: tba
 +  * ECTS credits: 3
 +  * Teaching hours: 72h
 +  * Type: advanced course
 +  * Language of instruction: French
 +  * Coordinator: tba
 +  * Instructor(s): François Brucker, Michaël Chalamel (L'Oréal), Franck Chevalier (EY), Emmanuel Daucé, Christophe Pouet
 +  * //Last update 24/03/2021 by C. Pouet//
 +
 +==== Brief description ====
 +
 +This course unit is divided into three 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.
 +
 +==== 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
 +
 +
 +==== Bibliography ====
 +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
 +
 +
 +
 +
 +
 +
 +
  
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