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en:ddefiprod [2021/08/26 11:17] – cpouet | en:ddefiprod [2021/08/27 10:27] (Version actuelle) – [Course metadata] cpouet | ||
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+ | =====Course unit: Data science project ===== | ||
+ | ==== Course metadata ==== | ||
+ | * Title in French: Projet data | ||
+ | * Course code: tba | ||
+ | * ECTS credits: 3 | ||
+ | * Teaching hours: 60h | ||
+ | * Type: advanced course | ||
+ | * Language of instruction: | ||
+ | * Coordinator: | ||
+ | * Instructor(s): | ||
+ | * //Last update 27/08/2021 by C. Pouet// | ||
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+ | ==== Brief description ==== | ||
+ | |||
+ | The course consists of a theoretical part and a practical part, simulating a business project. | ||
+ | |||
+ | ==== Learning outcomes ==== | ||
+ | |||
+ | * Understand the workflow of a data science project in a business context | ||
+ | * Be able to account for business (collection of needs, project lifecycle, communication) and technical (data, machine learning, scaling) constraints | ||
+ | |||
+ | ==== Course content ==== | ||
+ | - Data science in business | ||
+ | * The main issues | ||
+ | * Examples of data project | ||
+ | - Starting a data science project | ||
+ | * The constraints of data science projects | ||
+ | * Finding data | ||
+ | * Acquiring information | ||
+ | * Playing with data | ||
+ | - Lifecycle of a project | ||
+ | * The Bias-Variance tradeoff | ||
+ | * Feature Selection | ||
+ | * Feature Engineering | ||
+ | * Defining a metric | ||
+ | - The basic models | ||
+ | * Regressions (linear, polynomial, penalized et logistic) | ||
+ | * Decision trees (random forest and gradient boosting) | ||
+ | - Focus Natural Language Processing (NLP) | ||
+ | * Word Embedding | ||
+ | * Example: Sentiment analysis | ||
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+ | |||
+ | ==== Bibliography ==== | ||
+ | Check the availability of the books below at [[https:// | ||
+ | * Zeng, A and Casari, A. Feature Engineering for Machine Learning. O' | ||
+ | * Müller, A. and Guido, S. Introduction to Machine Learning with Python. O' | ||
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