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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: | ||
| + | * Coordinator: | ||
| + | * Instructor(s): | ||
| + | * //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 | ||
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| + | |||
| + | ==== Bibliography ==== | ||
| + | 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 | ||
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