Bachelor in Applied AI
Three years taught in English: two foundation years in Data & AI or Industrial IT, then a professional final year preparing a French Level 6 title.
Bachelor · 3 years · 100% English
From your first line of code to a State-registered title
Years 1 and 2 are AI2 foundation years, offered in two tracks: Data & AI or Industrial IT. Year 3 is the professional final year, with a company placement, preparing a French Ministry of Labour title at Level 6.
The programme map
Two tracks, one final-year choice
A Data & AI student can join the Industry 4.0 final year on academic review; the reverse requires the software prerequisites. Progression to each year requires validation of the previous one.
Which track is for you?
Data & AI: future developers and data analysts
For students who like mathematics and programming, want to build software and data products, and aim at AI application developer, data analyst or data scientist roles.
Industrial IT: engineers of the connected factory
For students drawn to electronics, networks and automation, who want to work in manufacturing, energy, logistics or smart buildings as industrial IT or IIoT engineers.
Track 1
Data & AI track
Data & AI Foundations
The two pillars every AI profile needs: the mathematics behind data and machine learning, and the tools used in industry.
Semester 1 · Mathematics, programming and data basics210 h
| Code | Module | What students learn | Block | Hours |
|---|---|---|---|---|
| B1.01 | Sums & Products | Summation and product notation, induction, classic results | Maths | 21 h |
| B1.02 | Logic & Reasoning | Propositional logic, quantifiers, proof techniques | Maths | 14 h |
| B1.03 | Linear Systems | Gaussian elimination, solving and interpreting linear systems | Maths | 21 h |
| B1.04 | Real Sequences | Convergence, limits, recursive sequences | Maths | 14 h |
| B1.05 | Descriptive StatisticsPython, spreadsheet | Indicators, distributions, reading and interpreting charts | Maths | 14 h |
| B1.06 | Introduction to Python ProgrammingPython 3, Jupyter, VS Code | Syntax, data types, control flow, functions, modules, notebooks | Data & AI | 21 h |
| B1.07 | Data Visualisation with PythonMatplotlib, Seaborn | Choosing the right chart for the message | Data & AI | 21 h |
| B1.08 | Code Versioning with Git & GitHubGit, GitHub | Local Git, branches, collaboration, GitHub and GitLab workflows | Data & AI | 14 h |
| B1.09 | Linux & Development EnvironmentUbuntu, Bash | Command line, shell scripting, environments and packages | Data & AI | 14 h |
| B1.10 | Databases & SQL ProgrammingPostgreSQL | Relational model, DBMS, queries, joins, aggregations | Data & AI | 21 h |
| B1.11 | Technical English | Technical reading, documentation, presentations in English | Professional | 21 h |
| B1.12 | Project ManagementTrello | Planning, roles, deliverables, first agile practices | Professional | 14 h |
Semester 2 · Analysis, applied data and a first project210 h
| Code | Module | What students learn | Block | Hours |
|---|---|---|---|---|
| B1.13 | Counting & Combinatorics | Permutations, combinations, counting principles | Maths | 14 h |
| B1.14 | Finite Probability Spaces | Events, conditional probability, independence, Bayes | Maths | 14 h |
| B1.15 | Algorithms & Data StructuresPython | Complexity, sorting and searching, lists, stacks, trees | Maths | 14 h |
| B1.16 | Real Functions of One Variable | Functions, derivatives, variations: the intuition behind gradient descent | Maths | 14 h |
| B1.17 | Object-Oriented Programming in PythonPython | Classes, objects, inheritance, clean and reusable code | Data & AI | 14 h |
| B1.18 | Data Analysis with PandasPandas, Plotly | SQL from Python, cleaning, exploratory analysis, interactive Plotly charts | Data & AI | 21 h |
| B1.19 | Excel for Data ScienceExcel | Formulas, pivot tables, data preparation and dashboards | Data & AI | 21 h |
| B1.20 | Web Applications with FlaskFlask | Routes, templates, forms, serving data through a web app | Data & AI | 14 h |
| B1.21 | Introduction to Numerical OptimisationNumPy | Minimising a function, link with machine-learning training | Data & AI | 14 h |
| B1.22 | UX/UI & Data StorytellingPower BI | Dashboard design, Power BI basics, telling a story with data | Data & AI | 14 h |
| B1.23 | Communicating & Presenting Results | Reports, slides and oral presentation of analyses | Professional | 21 h |
| B1.24 | Ethics, Law & GDPR | Data protection, responsible use of data and AI | Professional | 14 h |
| B1.25 | Year 1 Data Synthesis ProjectFull stack of the year | Team project on real data, from SQL to dashboard, defended before a jury | Project | 21 h |
Teams of 3–4 clean and analyse a real dataset, store it in PostgreSQL, build a Flask or Power BI front-end and present it to a jury.
Graded labs and mini-projects, end-of-semester examinations and the year project. Pass mark 10/20 per semester, resit in June.
Students who validate both semesters progress to Year 2. The Industrial IT track stays open with the networks prerequisites.
Tools of the year
Reason with logic and proof, solve linear systems, compute statistics and simple probabilities, understand derivatives and optimisation.
Write clean Python with functions and classes, use Git and Linux, query SQL, serve data through a small Flask app.
Load, clean and explore data with Pandas, build clear charts and dashboards, present findings in English.
Applied Data, BI & Cloud
A second analytics language, advanced SQL and NoSQL, business intelligence and cloud data, with the linear algebra and probability that machine learning relies on.
Semester 1 · Linear algebra, R, SQL and business intelligence182 h
| Code | Module | What students learn | Block | Hours |
|---|---|---|---|---|
| B2.01 | Advanced Descriptive StatisticsPython, R | Bivariate analysis, correlation, outliers, robust indicators | Maths | 14 h |
| B2.02 | Matrices | Matrix operations, inverse, determinant, data as matrices | Maths | 14 h |
| B2.03 | Vector Spaces | Bases, dimension, subspaces | Maths | 14 h |
| B2.04 | Linear Maps | Linear transformations, kernel and image, matrix representation | Maths | 14 h |
| B2.05 | Introduction to R ProgrammingR, RStudio | R syntax, data structures, functions, RStudio projects | Data & AI | 21 h |
| B2.06 | Data Analysis with the Tidyversetidyverse | dplyr, tidyr, reproducible data-wrangling pipelines | Data & AI | 14 h |
| B2.07 | Business IntelligencePower BI, Tableau | Excel, Power BI and Tableau: data models, KPIs, dashboards | Data & AI | 21 h |
| B2.08 | Advanced SQLPostgreSQL | Sub-queries, CTE, window functions, indexes, views, optimisation | Data & AI | 28 h |
| B2.09 | Big Data, NoSQL & MongoDBMongoDB | Document databases, big-data ecosystem, MongoDB certification preparation | Data & AI | 21 h |
| B2.10 | Marketing & Market Analysis | Market studies, quantitative analysis of customers and demand | Business | 21 h |
Semester 2 · Probability, optimisation, R applications and cloud168 h
| Code | Module | What students learn | Block | Hours |
|---|---|---|---|---|
| B2.11 | Optimisation & Operations ResearchPython | Linear programming, constraints, decision models | Maths | 14 h |
| B2.12 | Integration on an Interval | Integrals, primitives, numerical integration | Maths | 14 h |
| B2.13 | Discrete Random Variables | Expectation, variance, Bernoulli, binomial and Poisson laws | Maths | 14 h |
| B2.14 | Continuous Random Variables | Densities, uniform, exponential and normal laws | Maths | 14 h |
| B2.15 | Data Visualisation with Rggplot2 | Grammar of graphics, publication-quality charts | Data & AI | 14 h |
| B2.16 | R Shiny: Deploying AnalysesShiny | Interactive web apps for data, deployment on a server | Data & AI | 14 h |
| B2.17 | Building & Deploying R Packagesdevtools | Package structure, documentation, testing, distribution | Data & AI | 7 h |
| B2.18 | Web Scraping with Rrvest, httr | Collecting data from web pages and APIs, legal limits | Data & AI | 14 h |
| B2.19 | Cloud Data ManagementAWS, Azure | Storing and querying data on AWS and Azure | Data & AI | 21 h |
| B2.20 | Finance & Financial AnalysisExcel | Financial statements, ratios, analysing a company with data | Business | 21 h |
| B2.21 | Year 2 Data Synthesis ProjectFull stack of the year | Team project: data collection, BI dashboard or Shiny app, cloud deployment, jury | Project | 21 h |
Teams collect data, store it in SQL or MongoDB, analyse it in R or Python and ship a Power BI dashboard or a Shiny app on the cloud.
Graded labs, projects and end-of-semester examinations; pass mark 10/20 per semester.
BSc AI-Powered Application Development by default; BSc Data Analytics & Applied AI (Industry 4.0) on academic review.
Tools of the year
Handle matrices and linear maps, model uncertainty with random variables, solve a simple optimisation problem.
Advanced SQL with window functions, MongoDB documents, cloud storage, responsible web data collection.
Analyse in R and Python, build BI dashboards, deploy a Shiny app, read financial and market data.
Track 2
Industrial IT track
Computing, Networks & Automation
Programming, networks, databases, electricity and sensors, PLCs and how a factory runs, each module already linked to a competency block of the Year 3 title.
Computing, networks and data · six modules245 h
| Code | Module | What students learn | Block | Hours |
|---|---|---|---|---|
| IB1.01 | Mathematics for Computing & DataSpreadsheet, Python, Jupyter | Logic and Boolean algebra; binary and hexadecimal; sets and functions; descriptive statistics; basic probability; reading charts | Transv. | 35 h |
| IB1.02 | Algorithms & Python ProgrammingPython 3, VS Code, Jupyter | Algorithmic thinking; types and control flow; functions; lists, dictionaries, tuples; CSV and JSON; modules; debugging; first unit tests | BC01 · BC03 | 63 h |
| IB1.03 | Operating Systems & Work EnvironmentUbuntu, Windows 11, VirtualBox | Hardware architecture; Windows and Linux; Bash and PowerShell; users and rights; virtualisation; automation scripts | BC02 | 28 h |
| IB1.04 | Computer Network FundamentalsPacket Tracer, Wireshark | OSI and TCP/IP; Ethernet; IPv4 addressing and subnets; IPv6 basics; DHCP, DNS; switching; frame analysis; addressing plan of a small site | BC02 | 49 h |
| IB1.05 | Relational Databases & SQLPostgreSQL, DBeaver | Relational model; queries, joins, aggregates, sub-queries; DDL and DML; keys and constraints; first data modelling | BC03 | 42 h |
| IB1.09 | Front-end Web DevelopmentVS Code, Git, GitHub | HTML5, CSS3 and responsive layout; JavaScript and the DOM; forms; displaying data; Git basics | BC03 | 28 h |
Industrial systems and professional skills · six modules175 h
| Code | Module | What students learn | Block | Hours |
|---|---|---|---|---|
| IB1.06 | Electricity, Electronics & Industrial SensorsArduino, ESP32, multimeter, rigs | Electrical laws; electrical-risk awareness; discrete and analogue sensors (0–10 V, 4–20 mA); actuators; signal conditioning; microcontroller acquisition | BC01 · BC02 | 35 h |
| IB1.07 | Introduction to Automation & PLCsSiemens TIA Portal, PLCSIM | Combinational and sequential logic; GRAFCET; PLC architecture and scan cycle; Ladder; timers and counters; tests on rigs | BC01 | 49 h |
| IB1.08 | Industrial Organisation & Performance IndicatorsCase studies, spreadsheet | Production flows; Lean, 5S, continuous improvement; OEE, MTBF, MTTR, scrap rate; quality; Industry 4.0; the IT/OT boundary | BC01 | 28 h |
| IB1.10 | Cyber-hygiene, GDPR & Digital EthicsANSSI and CNIL guides | Threat landscape; strong authentication; backups; ANSSI hygiene guide; GDPR; specifics of industrial environments | BC02 | 21 h |
| IB1.11 | Technical & Professional EnglishManufacturer documentation | IT and industrial vocabulary; documentation; emails and tickets; short oral presentations | Transv. | 21 h |
| IB1.12 | Professional Communication & Project MethodOffice suite | Intervention reports; oral presentation; teamwork; disability awareness; launch of the pilot-line project | Transv. | 21 h |
Students wire sensors to a microcontroller, program a first PLC sequence and display measurements in a web page.
Graded labs, case studies, mini-projects and written tests, each module marked out of 20. Pass mark 10/20 per semester.
Electronics, PLC and network labs on physical rigs, in groups of 12 students at most.
Tools of the year
Write and test Python scripts, work in Linux and Windows, query SQL, build a web page that displays data.
Explain OSI and TCP/IP, design the addressing plan of a small site, analyse frames, apply the ANSSI hygiene rules.
Wire and read sensors, program a first PLC sequence in Ladder, compute OEE, MTBF and MTTR.
IT & OT Engineering
Both sides of the factory network: data engineering, reporting, APIs and machine learning on the IT side; automation, routing, VLANs and industrial protocols on the OT side.
IT side · data, databases, APIs and ML238 h
| Code | Module | What students learn | Block | Hours |
|---|---|---|---|---|
| IB2.01 | Advanced Python, OOP & VersioningPython, pytest, Git, GitHub | Object-oriented programming; packages; error handling; virtual environments; pytest; code quality; Git branches, reviews and merges | BC01 · BC03 | 42 h |
| IB2.02 | Data Engineering & Production Historisationpandas, InfluxDB, TimescaleDB | Machine-data acquisition; ETL with pandas; data quality; time series; time-series databases; scheduling; traceability | BC01 | 49 h |
| IB2.03 | Production Reporting & DashboardsPower BI, Grafana | KPIs defined with users; decision-support data models; Power Query, DAX basics; Grafana; automated reporting | BC01 | 42 h |
| IB2.07 | Database Modelling & ProgrammingPostgreSQL, Looping, DBeaver | Merise and UML; conceptual, logical and physical models; normalisation; indexes; views; stored procedures; triggers | BC03 | 42 h |
| IB2.08 | Back-end Development & APIsFastAPI, Postman | HTTP and REST; FastAPI; JSON; database connection; authentication; OpenAPI documentation; API testing | BC03 | 35 h |
| IB2.09 | Machine Learning for Industrial Datascikit-learn, Jupyter | ML project method; data preparation; regression and classification; anomaly detection; metrics; limits and bias | BC01 | 28 h |
OT side and professional skills182 h
| Code | Module | What students learn | Block | Hours |
|---|---|---|---|---|
| IB2.04 | Advanced Automation & HMITIA Portal, WinCC, PLCSIM | IEC 61131-3 (Ladder, ST, FBD); structured programs; introduction to PID; HMIs and operator panels; diagnosis | BC01 | 56 h |
| IB2.05 | Switching, Routing & VLANCisco IOS, Packet Tracer, GNS3 | VLAN and 802.1Q trunks; Spanning Tree; static routing and OSPF; inter-VLAN routing; access-control lists; documentation | BC02 | 49 h |
| IB2.06 | Industrial Protocols & IIoTNode-RED, UaExpert, Mosquitto | Modbus RTU/TCP; Profinet; EtherNet/IP; OPC UA; MQTT publish/subscribe; gateways; data flows from shop floor to IS | BC02 | 49 h |
| IB2.10 | Technical English | Specifications, standards and datasheets; manufacturer support; project presentation | Transv. | 14 h |
| IB2.11 | Agile Project ManagementJira, Trello | Scrum and Kanban; requirements; user stories; estimation; sprint follow-up; retrospective | Transv. | 14 h |
A simulated PLC, data over OPC UA or MQTT, a time-series database, a FastAPI service and a production dashboard, defended before a jury.
Graded labs, data and modelling files, practical examinations and the pilot-line project. Pass mark 10/20 per semester.
The final year on RNCP41370, with the company placement and the State examination.
Tools of the year
Build a pipeline from machine data to a time-series database, publish KPIs, expose data through FastAPI, train an anomaly model.
Program an IEC 61131-3 application with its HMI, configure VLANs and routing, connect equipment over Modbus, OPC UA and MQTT.
Deliver a connected pilot line end to end and defend it individually before a jury, in English.
Tuition and admission
Fees and entry requirements
| Year | Level | Tuition per year | From, after AI2 scholarship |
|---|---|---|---|
| Bachelor Year 1 · Data & AI or Industrial IT | Foundation | €9,000 | Stated in the admission letter |
| Bachelor Year 2 · Data & AI or Industrial IT | Foundation | €9,000 | Stated in the admission letter |
| BSc final year · App Development or Industry 4.0 | Level 6 · EQF 6 | €10,500 | €9,900 |
RequirementsWho can apply
- Year 1: secondary-school leaving certificate with mathematics, or science for Industrial IT.
- Year 2: one validated year of higher education in a related field.
- English B2: IELTS 6.0, TOEFL iBT 72 or English-medium schooling.
- File review and online interview. French is not required.
IncludedWhat your tuition covers
- Teaching, labs, projects and examinations.
- Google Workspace with Gemini.
- Work-study possible from Year 2, subject to a contract.
- Not included: housing, living costs, insurance, travel and visa fees.
Bachelor Years 1 and 2 are AI2 foundation years. The French State title is prepared in Year 3 and requires passing every competency block. See Life in France for living costs and work-study for the options from Year 2.