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<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24" class="iconify mr-1" data-icon="mdi:arrow-left" style="vertical-align: -0.125em; transform: rotate(360deg);"><path d="M20 11v2H8l5.5 5.5l-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5L8 11h12z" fill="currentColor"></path></svg>=====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 Méditerranée 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