Différences
Ci-dessous, les différences entre deux révisions de la page.
| Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente | ||
| en:ddefidode2022 [2022/07/04 15:27] – [Course metadata] cpouet | en:ddefidode2022 [2025/09/18 10:36] (Version actuelle) – [Course content] cpouet | ||
|---|---|---|---|
| Ligne 1: | Ligne 1: | ||
| + | =====Course unit: Data and Decisions ===== | ||
| + | ==== Course metadata ==== | ||
| + | * 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: | ||
| + | * Coordinator: | ||
| + | * Instructor(s): | ||
| + | * //Last update 02/09/2025 by C. Pouet// | ||
| + | |||
| + | ==== Brief description ==== | ||
| + | |||
| + | 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. | ||
| + | |||
| + | ==== 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 | ||
| + | - Recommendation systems (including KNN, PCA and SVD) | ||
| + | - Data visualization (including maps, geopandas, ...) | ||
| + | |||
| + | |||
| + | === 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:// | ||
| + | - 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 | ||
| + | |||
| + | |||
| + | |||
| + | |||
| + | |||
| + | |||
| + | |||