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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 | ||
+ | |||
+ | |||
+ | ==== 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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