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Ci-dessous, les différences entre deux révisions de la page.

--- en:ddefiprod [2021/03/24 10:32] – créée 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: French
+  * Coordinator: tba
+  * Instructor(s): Alexandre Chirié (Mantiks), Maximilien Defourné (Mantiks)
+  * //Last update 27/08/2021 by C. Pouet//
+==== 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
+==== Bibliography ====
+Check the availability of the books below at [[https://documentation.centrale-marseille.fr/|Centrale Marseille library]].
+  * Zeng, A and Casari, A. Feature Engineering for Machine Learning. O'Reilly Media.
+  * Müller, A. and Guido, S. Introduction to Machine Learning with Python. O'Reilly Media.