• 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

The course consists of a theoretical part and a practical part, simulating a business project.

  • 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
  1. Data science in business
    • The main issues
    • Examples of data project
  2. Starting a data science project
    • The constraints of data science projects
    • Finding data
    • Acquiring information
    • Playing with data
  3. Lifecycle of a project
    • The Bias-Variance tradeoff
    • Feature Selection
    • Feature Engineering
    • Defining a metric
  4. The basic models
    • Regressions (linear, polynomial, penalized et logistic)
    • Decision trees (random forest and gradient boosting)
  5. Focus Natural Language Processing (NLP)
    • Word Embedding
    • Example: Sentiment analysis

Check the availability of the books below at 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.
  • en/ddefiprod.txt
  • Dernière modification : 2021/08/27 10:27
  • de cpouet