MSc Applied AI & Data Science
Design industrial information systems and build AI solutions using machine learning, language models, computer vision and production deployment practices.
Programme overview
Design, deploy and industrialise AI systems within business and industrial processes, from data pipelines and information-system architecture to generative AI agents and model deployment.
| At a glance | Programme information |
|---|---|
| Teaching language | 100% English |
| Campus | Paris region, France |
| Study route | Full route: two years · direct Year 2 entry: 18 months |
| Professional qualification | RNCP39978 · Level 7 / EQF 7 |
| Direct Year 2 route | 12-month taught phase followed by a six-month company internship and final professional project defence |
Programme awards and certification
The AI2 programme diploma and the French RNCP professional title are distinct awards. The programme prepares for the title below. Award of the professional title requires validation of all required competency blocks and compliance with the certification’s assessment and eligibility rules.
| Professional title | RNCP reference | Qualification level | Certifying body |
|---|---|---|---|
| Architecte des systèmes d’information dans les processus industriels | RNCP39978 | Level 7 / EQF 7 | DATAKOO |
RNCP Level 7 describes a professional qualification level; it does not by itself confer the French national master’s degree or the grade de master. The shared MSc Year 1 is a foundation year and does not independently award the final RNCP title.
Career directions
The programme develops skills relevant to the following professional roles:
- Data scientist
- Machine-learning engineer
- AI solutions architect
- MLOps engineer
- AI product lead
Discuss your study plans
Our admissions team can help you identify the entry route that matches your qualifications.
admissions@ai2-education.com
+33 1 80 87 35 11
Shared MSc Year 1 · 500 hours
All three MSc routes share one foundation year across two semesters. Students develop programming, data and machine-learning skills before their Year 2 specialisation.
Semester 1 · Foundations
| Subject | Skills and content |
|---|---|
| Python for Data Science | Python, NumPy, Pandas, data cleaning, Matplotlib and Seaborn visualisation, Git and GitHub. |
| SQL & Databases | Data modelling, PostgreSQL, common table expressions, window functions, query optimisation and Python–SQL pipelines. |
| Introduction to Machine Learning | Supervised and unsupervised learning, model evaluation, feature engineering and scikit-learn. |
| Data Visualisation & Storytelling | Dashboards, business indicators and clear communication of insights to decision-makers. |
| Agile Project Management | Scrum, Kanban, product backlogs and collaborative delivery of data projects. |
Semester 2 · Applied AI
| Subject | Skills and content |
|---|---|
| Deep Learning Foundations | Neural networks, PyTorch and TensorFlow, training and tuning, and transfer learning. |
| AI for Industry | Predictive maintenance, quality control and process optimisation in industrial use cases. |
| Data Engineering | ETL and ELT pipelines, orchestration with Airflow, APIs and data quality. |
| Cloud & DevOps | Cloud services, Docker, CI/CD and deployment of data and AI services. |
| AI Regulation & Ethics | EU AI Act, GDPR, responsible AI, bias and explainability. |
| Industrial Team Project | Develop a pipeline, model, dashboard and report, then present the deployed application before a jury. |
Year 2 specialisation · 500 hours
Year 2 combines the four RNCP39978 competency blocks, specialist subjects, business-data skills and final examinations.
| Component | Subject | Detailed content | Teaching hours |
|---|---|---|---|
| Block 1 | Industrial IS analysis | Advanced studies and business analysis; regulatory compliance; industrial process mapping and risk analysis using MITRE ATT&CK for ICS and IEC 62443; block defence. | 56 h |
| Block 2 | Architecture & cybersecurity | Cloud, IoT and edge computing; advanced security architecture, zero trust and EBIOS RM; networks, applied GDPR and block defence. | 56 h |
| AI track 1 | NLP & Transformers | Text processing, embeddings, Transformer architectures, fine-tuning and information extraction. | 42 h |
| AI track 1 | Advanced Computer Vision | CNNs and vision Transformers, detection and segmentation, and industrial quality control. | 42 h |
| Transversal | Business Data | Business intelligence, data strategy and value, KPIs, and data storytelling for executives throughout the year. | 84 h |
| Block 3 | Prototyping & simulation | Connected HMI prototypes, data-flow modelling, real-time simulation with MQTT and WebSockets, real-time API integration and digital twins; block defence with a live demo. | 56 h |
| Block 4 | Data & AI innovation | Advanced industrial machine learning and deep learning, predictive maintenance, generative AI and synthetic data, ethics and explainability; block defence with a live demo. | 56 h |
| AI track 2 | Generative AI & Agents | LLMs, RAG architectures, autonomous agents, multi-agent systems, evaluation and guardrails. | 42 h |
| AI track 2 | MLOps | Model lifecycle, experiment tracking, deployment, monitoring and drift, and CI/CD for machine learning. | 42 h |
| Assessment | Final examinations | Eight written and practical examinations over four days, covering the four blocks and four specialisation subjects. | 24 h |
| Total | Year 2 | Specialisation and examinations | 500 h |
RNCP39978 competency blocks
| Block | Competency |
|---|---|
| Block 1 | Systemic needs analysis of industrial information systems. |
| Block 2 | Design of the information-system architecture. |
| Block 3 | Prototyping and simulation. |
| Block 4 | Improvement through data and AI innovation. |
Professional project and assessment
Develop a generative-AI project using real professional data, prototype the solution and defend it individually. Assessment combines a defence for each competency block, final written and practical examinations, and a final professional project addressing a company problem.
The published MSc framework specifies a pass mark of 10/20 for each block defence. All required competency blocks must be validated under the certification assessment rules.
Company experience and final defence
Direct Year 2 entry follows a 12-month taught phase and a six-month company internship, including the final professional project defence. In the full route, specialisation follows the shared Year 1. A work-study alternative may be available from the second year in France, subject to eligibility and an employer contract.
Teaching and academic support
Explore the published AI2 academic profiles and subject expertise. Contact the academic team for the instructors assigned to your intake and modules.
Tuition for the 2027 international portfolio
| Entry year / programme stage | Annual tuition | From, after a conditional AI2 scholarship |
|---|---|---|
| MSc Year 1 · Applied AI common core | €12,500 | €10,500 |
| MSc Year 2 · MSc Applied AI & Data Science | €13,000 | €11,300 |
Tuition is charged per academic year. Scholarship awards apply to the first year of enrolment, are non-transferable and cannot be combined with other discounts. The award and its conditions are confirmed individually in the admission letter. Assessment for a scholarship does not guarantee an award.
For entry in MSc Year 1, the scholarship reference is the Year 1 amount. The Year 2 “from” amount applies when direct Year 2 is the first year of enrolment and a scholarship is awarded.
Funding and financial planning
Work-study may be available from the second year in France, subject to eligibility and a separate employer contract. Confirm the tuition funding and employment conditions in writing. Admission does not guarantee a host employer.
Before enrolling, request the applicable deposit, payment schedule and any additional costs. Plan separately for accommodation, transport, insurance and living expenses.
Tuition and scholarship information · Work-study information
Entry requirements
| Requirement | Programme information |
|---|---|
| Academic entry | Year 1: a three-year Bachelor’s degree in computing, mathematics, engineering or science. Direct Year 2: a related four- or five-year degree, subject to academic review. |
| English | B2 level: IELTS 6.0, TOEFL iBT 72 or equivalent. A medium-of-instruction certificate may support a waiver when the previous degree was taught in English; admissions confirms the evidence accepted. |
| French | French is not an admission requirement for the international portfolio. |
| Selection | Academic file review and an online interview. GRE and GMAT are not required by the portfolio. |
Application documents
- Passport; degree certificates and academic transcripts, with certified translations where needed.
- CV and a one-page statement of purpose.
- Proof of English level or a medium-of-instruction certificate.
- For direct Year 2: the course list or syllabus of the previous degree for academic review.
Application process
| Step | What happens |
|---|---|
| 1 · Submit your file | Choose your programme and entry route, then submit the complete application. |
| 2 · Academic review | AI2 reviews the qualifications and confirms the appropriate entry level. |
| 3 · Online interview | Discuss your background, preparation and study plans. |
| 4 · Admission decision | The admission letter confirms the offer, programme, intake, tuition and any scholarship. |
| 5 · Confirm enrolment | Follow the enrolment and deposit instructions in the offer. |
| 6 · Prepare arrival | Complete the relevant Campus France and visa steps, then arrange arrival and welcome. |
2027 intakes
January, May and September 2027 intakes are planned in the international portfolio. Programme availability and your entry route are confirmed in the admission letter.
January 2027 intake · published application calendar
| Stage | Date |
|---|---|
| Applications open | 1 September 2026 |
| Priority application deadline | 31 October 2026 |
| Complete visa file | 30 November 2026 |
| Enrolment and deposit | 15 December 2026 |
Contact admissions for the current application deadlines and arrangements for May and September 2027. Visa decisions are made by the French authorities; admission does not guarantee visa approval.
Student support
The portfolio describes an academic adviser, housing guidance, optional French classes, preparation for internship and work-study searches, and CV and interview coaching. Contact the team for the arrangements in your intake.