=====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.