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| en:ddefiando2024 [2024/07/26 11:31] – créée cpouet | en:ddefiando2024 [2026/05/22 15:48] (Version actuelle) – [Course content] cpouet | ||
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| + | =====Course unit: Data and analytics ===== | ||
| + | ** <color red> Beware! Under construction. </ | ||
| + | ==== Course metadata ==== | ||
| + | * Title in French: Analyses et données | ||
| + | * Course code: tba | ||
| + | * Type: specialized course | ||
| + | * ECTS credits: 4 | ||
| + | * Semester 10 (Spring) | ||
| + | * Teaching period: Mid-February to Mid-April | ||
| + | * Teaching hours: 100h | ||
| + | * Language of instruction: | ||
| + | * Coordinator: | ||
| + | * Instructor(s): | ||
| + | * //Last update 22/05/2026 by C. Pouet// | ||
| + | |||
| + | ==== Brief description ==== | ||
| + | |||
| + | This course unit is divided into four parts: | ||
| + | * **Quantitative marketing** (24 hours) taught by Simone Fuscone and Vincent Archer, | ||
| + | * **IA and decisions** (24 hours) taught by Franck Chevalier, | ||
| + | * **Applied data science** (24 hours) taught by Nathan Rouff and Antoine Winckels, | ||
| + | * **Data Project: | ||
| + | |||
| + | ==== Learning outcomes ==== | ||
| + | |||
| + | * Know how to use data in a strategic approach | ||
| + | * Know how to present a model, its results and its insights | ||
| + | * Know how to assess data suitability to a specific issue | ||
| + | * Know how to model intertemporal strategic decisions | ||
| + | * Know how to combine model and data to take pricing decisions | ||
| + | |||
| + | ==== Course content ==== | ||
| + | === Quantitative marketing=== | ||
| + | - Introduction to prescriptive analytics | ||
| + | - Interpretability and maching learning | ||
| + | - Application to revenue management | ||
| + | - Application to predictive maintenance | ||
| + | === Data and macroeconomics === | ||
| + | This course aims at giving a broad view of macroeconomic data. It is structured around three questions: | ||
| + | - Can we measure everything? | ||
| + | - Can we sum everything? | ||
| + | - Can we compare everything? | ||
| + | These questions will allow to tackle multiple sources for macroeconomic data, their methodology, | ||
| + | === Applied data science=== | ||
| + | - Introduction to prescriptive analytics | ||
| + | - Interpretability and maching learning | ||
| + | - Application to revenue management | ||
| + | - Application to predictive maintenance | ||
| + | |||
| + | === Data Project: | ||
| + | - Projects and models | ||
| + | * The Bias-Variance tradeoff | ||
| + | * Feature Selection | ||
| + | * Feature Engineering | ||
| + | * Defining a metric | ||
| + | - Models and applications | ||
| + | * Regressions (linear, polynomial, penalized and logistic) | ||
| + | * Decision trees (random forest and gradient boosting) | ||
| + | - Focus on Natural Language Processing (NLP) | ||
| + | |||
| + | ==== Bibliography ==== | ||
| + | You can check the availability of the books below at [[https:// | ||
| + | - Quantitatve marketing | ||
| + | * Abiteboul, S., « Sciences des données : de la logique du premier ordre à la Toile », Leçon inaugurale du Collège de France : [[https:// | ||
| + | - Data and macroeconomics | ||
| + | * [[https:// | ||
| + | * [[https:// | ||
| + | * [[https:// | ||
| + | - Yield management | ||
| + | * Sorger, G. Reference price formation and optimal marketing strategies, In Optimal Control Theory and Economic Analysis 3, G. Feichtinger (editor), Elsevier Science Publishers (North-Holland, | ||
| + | * Talluri, K. T., Van Ryzin, G. J., The Theory and Practice of Revenue Management, Springer 2004. | ||
| + | * Belobaba, Peter. 16.75J Airline Management, Spring 2006. MIT OpenCourseWare. | ||
| + | * Frumin, M., and Ben-Akiva, M. [[https:// | ||
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