Data Science and Modelling Programme

This course is eligible

www.moncompteformation.gouv.fr

Prerequisites

A good knowledge of mathematical tools (linear algebra, probability, statistics) and programming (Python).

Upcoming Intakes

9 and 10 January, 23 and 24 January, 6 and 7 February, 20 and 21 February, 6 and 7 March and 20 and 21 March 2026

Price

5,600 euros excluding VAT (eligible for funding from the CPF and Pôle Emploi)

Location

Paris-Villejuif Campus

Certification

This course leads to the validation of the RNCP38584BC03 skills module – Implementing a machine learning and deep learning solution – and the Level 7 professional certification ‘Project Manager in Artificial Intelligence Solutions Development’, awarded by aivancity and registered with the RNCP under number 38584 (on 9 February 2024).

This qualification can be accrued over a period of 5 years

You can choose to work towards the full qualification gradually over time or to have your additional skills recognised through the VAE.

Info

Find out about the programme’s assessment and teaching methods  

Click here to find out about the programme’s assessment and teaching methods  

Performance indicators 2024–2025

Content updated on 18 November 2025

100%

Satisfaction rate

100%

Graduation rate

100%

Degree pass rate

Target audience and overview

This programme is designed for professionals in the fields of data science and IT, or for any scientist or individual with a technical or managerial background who has prior knowledge of linear algebra, probability, statistics and programming (Python), and who wish to acquire practical skills in data science and machine learning.

This programme enables participants to discover how to deploy artificial intelligence solutions in an ethical and responsible manner. It also aims to strengthen and foster collaboration between scientific teams and business departments.

The survey published in “The Human Impact of Data Literacy” in 2020 [1] reveals that 74 per cent of employees surveyed (9,000 worldwide) feel overwhelmed by tasks involving their company’s data. This results in a decline in interest and productivity: 14 per cent of them would even prefer to avoid this type of work altogether.

One of the major challenges for a company seeking to make the most of its data is to standardise ‘data literacy’ across the organisation. To achieve this, managers must understand the potential of their data and its ethical implications, and know how to support their employees in developing their data skills (including the adoption of new technologies and software).

[1] The Data Literacy Project, “The Human Impact of Data Literacy”, 2020 https://www.accenture.com/content/dam/accenture/final/a-com-migration/r3-3/pdf/pdf-118/accenture-the-human-impact-data-literacy.pdf (Accenture)

Objectives

This course will enable you to acquire:

The skills to implement data solutions that are strategic for your business.
Practical experience with machine learning models and their applications to take your business to the next level.
A critical perspective on today’s challenges relating to artificial intelligence, particularly its ethical, legal and social implications.

Contents

Module 1

Data-driven decision-making

An introduction to data visualisation and exploratory data analysis, followed by an exploration of the power of data and its role in business decision-making.
The main concepts covered in this module are: data-driven decision-making, data cleaning, data coding and data visualisation using Python.

Module 2

Traditional machine learning algorithms

To learn about and use the fundamental techniques of machine learning, and to be able to assess the impact of such tools within a business.
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 machine learning

To explore the main challenges of machine learning, and the ethical and legal considerations surrounding the use of data in the business world.
The main concepts covered in this module are: model evaluation, performance analysis, overfitting versus underfitting, data augmentation, ensemble methods, and the ethical, legal and social issues surrounding the use of data.