Programs | Academic Page

Professors' College Committee

Sun, 19 Jul 2026 12:00:00 +0200

As an elected member of the college of Full Professors (Professeur) and Professors (Directeur d’études), this covers participation in recruitment juries, discipline councils, and discussions shaping the school’s strategic orientations for teaching.

Data Science Department

Sat, 18 Jul 2026 12:00:00 +0200

Under the department head’s direction, the Deputy of Teaching maintains a cross-program, qualitative and quantitative view of the department’s teaching engagement, and oversees the distribution of teaching workload across the department’s faculty. This includes working with course leads to identify teaching resources, relaying the Vice-Presidency for Education’s (DFVS) directions to department members and monitoring their implementation, and representing the department’s own constraints and program proposals to the DFVS.

The role also covers participation in program steering committees and in the department’s internal process ahead of teacher-researcher recruitment. Together with the DFVS and pole leads, Deputies of Teaching collectively build the program’s course catalog, limiting redundancy to keep teaching load manageable for pedagogical teams, and represent the department head in teaching-related bodies.

Data Science Program (TAF)

Sat, 18 Jul 2026 12:00:00 +0200

The Data Science TAF trains IMT Atlantique engineers to make the most of the data they will encounter in their careers: framing value-creation challenges, identifying the relevant data, managing it, building the processing and analysis systems it requires, and turning the results into summaries aligned with business strategy. The program’s core teaching units span four areas: applied statistics, machine learning, the computer science tools and environments data science relies on, and the economic, legal, and societal dimensions of working with data.

Students then choose one of three specialization pathways. Data Science & Machine Learning focuses on designing and industrializing predictive models, from validation through to production deployment. Data & Analytics centers on exploratory analysis, data visualization, and building the decision-making dashboards that support business decisions. Data Engineering & Architecture addresses the infrastructure side: distributed architectures for large-scale data processing, robust and scalable data pipelines, and data quality and security.

Across all three pathways, the pedagogy is hands-on: students apply what they learn to real cases across a range of business domains, building concrete expertise in data management and analysis alongside the tools that support it.