Majeure
Data and AI

Diplôme

Diplôme d'ingénieur (Bac+5)

Campus

Montpellier

Admission

Bac+4

Rythme

Alternance

Plein temps

Langue

Anglais

Stage

2 semestres

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Design intelligent systems, transform data into decisions, and build AI solutions for real-world impact.

In this major, you will learn how to design, develop and deploy intelligent data-driven systems for real-world applications.

Combining their generalist education with an expertise on all the Data Journey, the graduates will be able to addressall the missions of the Data Analyst: data recovery, structuration, analysis & reporting, in direct interaction with the enduser. They will also have a strong suit in Artificial Intelligence and Machine Learning allowing them to create models and advanced tools (predicter, classifier, etc.) and in Data Engineering, enabling them to collaboratively develop services using a v lue-driven, short-cycle approach and then deploy these services in the enterprise IT architectures.

Students have the option of completing the Major cycle (4th and 5th year) as an apprentice, thanks to the FISEA program. (Initial training under apprentice student status).

Brochures

JPO

Program

CORE COURSES

Course description: The course provides a solid foundation in software development, IT infrastructure, and web programming. Students learn how to develop applications collaboratively, efficiently adopt new programming languages and technologies, and produce robust, reusable software. The course covers key infrastructure concepts such as Linux sys The module is complemented by a professional development program featuring thematic activities focused on research, entrepreneurship, and innovation for social inclusion and disability support. tems, virtualization, networking, scripting, and the deployment of services on remote servers. It also introduces full-stack web development, including APIs, databases, user interfaces, security, and application deployment. By combining programming, systems administration, and web technologies, the course equips students with the practical skills required to build and manage modern digital solutions.

Max times this course can be taken for credit: 1
Credits: 5 ECTS, 2 units (international)
Mode of delivery:
- Lectures: 16,5 hours
- Laboratories and guided studies: 61,5 hours
Language of instruction: English

Course description: The course equips students with the skills needed to design, deploy, and operate modern data systems, from data modeling to infrastructure automation. Students learn how to build data architectures tailored to business needs, including transactional databases, data warehouses, data lakes, and distributed systems. The course covers data models, Big Data processing (with PySpark), distributed architectures, and DevOps practices using Docker, and service orchestration. Learners also gain hands-on experience in deploying scalable data services, managing databases, and automating production environments. By the end of the course, students are able to develop robust, scalable, and automated data solutions for modern digital organizations

Max times this course can be taken for credit: 1
Credits: 5 ECTS, 4 units (international)
Mode of delivery:
- Lectures: 40,5 hours
- Laboratories and guided studies: 41 hours
Language of instruction: English

Course description: This course enables students to transform data into actionable insights to guide decision-making. It covers the fundamentals of probability and statistics, data manipulation and visualization using Python (NumPy, Pandas, Plotly), as well as the principles of business intelligence and dashboard creation. Students learn to explore datasets, build meaningful visualizations, generate key performance indicators (KPIs), and conduct quantitative analyses to address real-world problems. The module also emphasizes critical thinking when analyzing data and the use of decision-making tools such as Power BI. Upon completion of the course, students will be able to produce reliable analyses, effectively communicate results, and support data-driven strategic decision-making.

Max times this course can be taken for credit:  1
Credits: 6 ECTS, 4 units (international)
Mode of delivery:
- Lectures: 28,5 hours
- Laboratories and guided studies: 79,5 hours
Language of instruction: English

Course description: The course provides students with the foundational knowledge required to build, evaluate, and apply predictive models from data. It covers the mathematical foundations of machine learning, including linear algebra, distance metrics, matrix factorization, and numerical optimization, as well as data cleaning and integrity techniques. Students learn how to identify and correct data quality issues, manage missing values, and handle outliers to prepare reliable datasets. The course also introduces key machine learning concepts such as feature engineering, model selection, overfitting prevention, and supervised learning algorithms including regression and tree-based methods. Through hands-on projects using Python and machine learning libraries, students gain practical experience in selecting, implementing, and assessing models for real-world data-driven applications.

Max times this course can be taken for credit:  1
Credits: 5 ECTS, 3 units (international)
Mode of delivery:
- Lectures: 34,5 hours
- Laboratories and guided studies: 50,5 hours
Language of instruction:English

Course description: The course develops the key professional skills required for successful engineering projects and career development. It covers contract fundamentals. The course also strengthens professional communication skills through an English and TOEIC preparation program focused on business vocabulary, listening, reading, and speaking abilities. The module is complemented by a professional development program featuring thematic activities focused on research, entrepreneurship, and innovation for social inclusion and disability support. By the end of the module, students are equipped to work effectively in international, collaborative, and project-driven environments.

Max times this course can be taken for credit: 1
Credits: 3 ECTS, 3 units (international)
Mode of delivery:
- Lectures: 3,5 hours
- Laboratories and guided studies: 20,5 hours
Language of instruction: English

ELECTIVE COURSES

Course description: The course immerses students in a real-world professional environment where they design and develop a solution for an external client as part of a team. The course covers traditional and agile project management methods, as well as teamwork and collaborative development practices. Students learn how to plan and manage projects, facilitate effective meetings, organize team efforts, and deliver solutions that meet real client needs Built around a learning-by-doing approach, it applies agile project management, stakeholder collaboration, communication, and continuous improvement practices. Students learn to lead projects from inception to delivery, create value for clients, and ensure the quality, maintainability, and positive impact of the final product or service. By the end of the course, students have strengthened their leadership, professional behavior, and ability to deliver successful projects in collaborative and complex environments

Max times this course can be taken for credit: 1
Credits: 6 ECTS, 4 units (international)
Mode of delivery:
- Lectures: 24,5 hours
- Laboratories and guided studies: 123 hours
Language of instruction: English

Corporate assignment

CORE COURSES

Course description: The course introduces students to the design, deployment, and operation of cloud-native applications and data services. The course covers cloud computing architectures, public and private cloud environments, agentic software development and complex web services architectures (API, microservice). Through hands-on projects, they develop practical skills in cloud infrastructure, service orchestration, software maintenance, and the lifecycle management of data-driven applications. The course prepares students to design and operate reliable, scalable, and maintainable SaaS platforms in professional environments.

Max times this course can be taken for credit: 1
Credits: 5 ECTS, 3 units (international)
Mode of delivery:
- Lectures: 39 hours
- Laboratories and guided studies: 36 hours
Language of instruction: English

Course description: The course provides a comprehensive exploration of modern machine learning techniques, from foundational predictive models to advanced deep learning and generative AI applications. Students learn to design, implement, evaluate, and improve machine learning solutions for real-world problems using state-of-the-art tools and frameworks. The course covers supervised and unsupervised learning, deep neural networks, natural language processing, large language models (LLMs), and model deployment in production environments. Through practical projects and hands-on laboratories, students develop the skills required to build, optimize, and operationalize intelligent systems while addressing challenges related to performance, scalability, and model evolution.

Max times this course can be taken for credit: 1
Credits: 5 ECTS, 3 units (international)
Mode of delivery:
- Lectures: 22,5 hours
- Laboratories and guided studies: 56,5 hours
Language of instruction: English

Course description: The course equips students with the skills required to transform data into a strategic asset for organizations. The course combines Analytics, Data Governance, Data Quality Management, and Cybersecurity to support data-driven decision-making. Students learn how to implement reporting processes, devises and monitor Business Strategies, develop data governance frameworks, and assess data quality. The curriculum also covers information security, cyber threat detection, risk management, and data protection practices. Through practical projects and case studies, students gain the ability to align data, technology, and business objectives while ensuring the reliability, security, and value of organizational data.

Max times this course can be taken for credit: 1
Credits: 5 ECTS, 4 units (international)
Mode of delivery:
- Lectures: 42 hours
- Laboratories and guided studies: 36 hours
Language of instruction: English

Course description: The course explores the ethical, legal, societal, and environmental dimensions of data-driven technologies. The course examines data ethics, algorithmic bias, explainable AI, data protection regulations, data ownership, and governance frameworks. Students learn to identify ethical and legal risks in data projects, assess the societal and environmental impacts of digital systems, and propose responsible design improvements. The curriculum also addresses sustainability, eco-design principles, life-cycle assessment, and the limitations and biases inherent in data collection and modeling. Through practical case studies and critical analysis, students develop the skills required to design and deploy data solutions that are responsible, transparent, compliant, and socially conscious.

Max times this course can be taken for credit: 1
Credits: 4 ECTS, 3 units (international)
Mode of delivery:
- Lectures: 35 hours
- Laboratories and guided studies: 34 hours
Language of instruction: English

Course description: The course strengthens students’ expertise in advanced machine learning methods and their practical application to real-world data challenges. The course covers time series analysis and forecasting, graph mining and graph neural networks, and computer vision with deep learning. It also covers the transition from prototypes to production-ready systems. Students learn to model complex datasets, extract meaningful patterns, improve predictive performance, and deploy machine learning solutions across diverse domains. Through hands-on projects and practical laboratories, they develop advanced skills in data representation, predictive analytics, image processing, and learning from structured and unstructured data. Students also learn to deploy scalable services, build and manage data pipelines, integrate AI-powered solutions, and implement modern DevOps and MLOps practices. The course prepares students for engineering roles focused on the design, implementation, and optimization of modern machine learning systems.

Max times this course can be taken for credit: 1
Credits: 5 ECTS, 2 units (international)
Mode of delivery:
- Lectures: 7,5 hours
- Laboratories and guided studies: 75 hours
Language of instruction: English

ELECTIFS

Course description: The course is a large-scale, project-based learning experience conducted in collaboration with a real client. Working in teams, students apply agile project management methods to design, develop, and deliver a product or service that creates measurable value for stakeholders. The course emphasizes collaboration, client interaction, continuous improvement, quality assurance, and responsible project delivery. Students strengthen their professional skills through coaching sessions focused on career planning, employability, and personal development. By combining technical execution, teamwork, and professionalization, the module prepares students to lead complex projects and transition effectively into the workplace.

Max times this course can be taken for credit: 1
Credits: 6 ECTS, 4 units (international)
Mode of delivery:
- Lectures: 9,5 hours
- Laboratories and guided studies: 142,5 hours
Language of instruction: English

Corporate assignment

Learn by building

A project is carried out during both academic semesters in collaboration with a company. It is used as a guideline for the whole semester and serves as support to many lectures. Some examples of projects conducted in the major.