Data Science and Modeling

Program Overview

This certification program in Data Science and Modeling equips learners with the skills to implement data-driven solutions, leverage machine learning models, and navigate ethical and legal considerations in AI. It covers data visualization, machine learning algorithms, and the challenges and limitations of AI. The program is led by renowned faculty and prepares learners for careers in AI project management.

Program Outline

Degree Overview:

Data Science and Modeling

Overview:

This certification corresponds to a block of competences whose title reads "Artificial Intelligence Project Manager", currently under instruction at France Compétences. It will lead to a certification recognized and registered in the Répertoire national des certifications professionnelles (RNCP) and will therefore be eligible for the CPF. This certification can be capitalized on over 5 years. You can choose to prepare the entire title progressively over time or have your complementary skills recognized by the VAE.

Objectives:

• Acquire skills to implement data solutions, strategic for the company.
• Gain concrete experience with machine learning models and their applications to give a new dimension to the company.
• Acquire a critical look at today's issues related to artificial intelligence, and in particular its ethical, legal and social stakes.

Outline:

Module 1: Data-driven Decision Making

• Take control of data visualization, exploratory data analysis, then exploit the power of data and its role in corporate decision-making.
• The main concepts covered in this module are: decision making using data, data cleansing, data coding and data visualization using Python.

Module 2: Classical machine learning algorithms

• To learn to know and use the fundamental techniques of machine learning, and to know how to gauge the impact of such tools in a company.
• The main concepts covered in this module are: time series, clustering (data partitioning), and regression methods using scikit-learn.

Module 3: Ethics, bias and limitations of the learning machine

• Explore the main challenges of machine learning, and the ethical and legal considerations of data use in the business world.
• The main concepts covered in this module are: model evaluation, performance analysis, overlearning vs. underlearning, data augmentation, ensemble methods, and the ethical, legal and social issues of data use.

Teaching:

Faculty:

• Doreid Ammar, Professor in Data science and Computer science | Academic Director
• Ysens de France, Associate Professor of AI Law | Director of Foresight at the Sapiens Institute
• Emmanuel R. Goffi, Associate Professor of AI Ethics and Director of International Relations | Director of the Ethics & IA Observatory - Sapiens Institute
• Hugo Hadjur, Assistant Professor in IA & Data Science | PhD student at ENS Lyon and aivancity
• Virginie Mathivet, Expert AI & Deep learning teacher | Director of R&D, Corporate Teamwork
• Levente Szabados, Visiting professor in Data science | Lecturer at Frankfurt school of finance & management