Différences
Ci-dessous, les différences entre deux révisions de la page.
| Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente | ||
| en:ddefidode2022 [2024/06/28 15:18] – modification externe 127.0.0.1 | en:ddefidode2022 [2025/09/18 10:36] (Version actuelle) – [Course content] cpouet | ||
|---|---|---|---|
| Ligne 3: | Ligne 3: | ||
| * Title in French: Données et Décisions | * Title in French: Données et Décisions | ||
| * Course code: tba | * Course code: tba | ||
| - | * ECTS credits: | + | * ECTS credits: |
| * Type: advanced course | * Type: advanced course | ||
| * Semester 9 (Fall-Winter) | * Semester 9 (Fall-Winter) | ||
| Ligne 10: | Ligne 10: | ||
| * Language of instruction: | * Language of instruction: | ||
| * Coordinator: | * Coordinator: | ||
| - | * Instructor(s): | + | * Instructor(s): |
| - | * //Last update | + | * //Last update |
| ==== Brief description ==== | ==== Brief description ==== | ||
| This course unit is divided into four parts: | This course unit is divided into four parts: | ||
| - | * ** Statistical learning ** (24 hours) taught by Christophe Pouet. | + | * ** Statistical learning ** (30 hours) taught by Christophe Pouet. |
| - | * ** Python for data science ** (24 hours) taught by François Brucker and Emmanuel Daucé. | + | * ** 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. | * ** Advising using data ** (24 hours) taught by Michaël Chalamel and Franck Chevalier. | ||
| - | * ** Data Project: data sources and preprocessing ** (24 hours) taught by tba. | + | * ** Data Project: data sources and preprocessing ** (24 hours) taught by Sitraka Forler and Lirone Samoun. |
| ==== Learning outcomes ==== | ==== Learning outcomes ==== | ||
| Ligne 46: | Ligne 46: | ||
| === Python for data science=== | === Python for data science=== | ||
| - Dataframe: data exploration and data description | - Dataframe: data exploration and data description | ||
| - | | + | |
| - | - Principal Component Analysis | + | - Data visualization (including maps, geopandas, ...) |
| - | - Correspondence analysis | + | |
| - | - Prediction using trend analysis | + | |
| - | - Linear regression | + | |
| - | - Logistic regression | + | |
| - | - Data classification | + | |
| - | - Classification using partitions | + | |
| - | - Hierarchical methods | + | |
| === Data-driven decision making=== | === Data-driven decision making=== | ||
| - What is data? | - What is data? | ||
| Ligne 65: | Ligne 60: | ||
| ==== Bibliography ==== | ==== Bibliography ==== | ||
| - | You can check the availability of the books below at [[https://documentation.centrale-marseille.fr/ | + | You can check the availability of the books below at [[https://www.centrale-mediterranee.fr/fr/ |
| - Statistical Learning | - Statistical Learning | ||
| * James G., Witten D., Hastie T. and al. (2013). An introduction to statistical learning: with applications in R. New York: Springer | * James G., Witten D., Hastie T. and al. (2013). An introduction to statistical learning: with applications in R. New York: Springer | ||