A learning path built for you
Write what you want to be able to do. Your path is yours alone: it is assembled from 114 courses, then every lesson is rewritten with examples from your job, at your level, in your language.
- One goal, one path
- Examples from your field
- It adapts to your mistakes
One lesson, one version per learner
The concepts never change. The explanation, examples and exercises do. Pick a profile to see the “Lists and loops” lesson told another way.
Bases de la programmation et algorithmiqueLesson 3 of 520 min
Listes et boucles
Profile · Data analysisAnalyse de données en entreprise (ventes, marketing, finance)
Personalisation briefTravailler sur des listes de chiffres d'affaires mensuels, de montants de commandes ou de températures.
Example for this profile · Total et moyenne des ventes
ventes = [1200, 950, 1430, 1100]
total = 0
for v in ventes:
total += v
print(total, total / len(ventes))Motif accumulateur : total vaut 4680 et la moyenne 1170.0.
Profile · Online shopSite e-commerce (catalogue, panier, commandes)
Personalisation briefParcourir un panier (liste de prix) pour calculer le total et compter les articles.
Example for this profile · Compter les produits en rupture
stocks = [5, 0, 12, 0, 3]
ruptures = 0
for s in stocks:
if s == 0:
ruptures += 1
print(f"{ruptures} produits en rupture")Motif compteur combiné à une condition.
Mandatory points
The same for every profile
- Une liste est une collection ordonnée ; les indices commencent à 0 et -1 désigne le dernier élément
- len() donne la taille d'une liste et append() ajoute un élément à la fin
- La boucle for parcourt chaque élément d'une séquence ; range(n) produit 0 à n-1
- La boucle while répète tant qu'une condition est vraie et doit pouvoir s'arrêter
- Le motif accumulateur : initialiser avant la boucle, mettre à jour dans la boucle
Exercise F1-L03-E01
Somme d'une liste
3 tests run in the sandbox before display
How your path is built
The AI never writes the curriculum. It picks lessons that already exist, tells them for you, and everything is checked before it reaches your screen.
The catalogue
583 written lessons, tagged by skill, prerequisite, level and duration.
F1-L03fondamentaux.bouclesdébutant · 20 minSelection
The AI orders existing lessons. The server checks every identifier and every prerequisite.
Start of the Web developer (PHP) trackF1F2F3F5FE1FE2Rewriting
Explanations, examples and exercises are rewritten in your language and for your context.
F1-L03web_ecommercefrReview
A second pass checks that every mandatory point of the lesson is covered.
5 mandatory pointsSandbox
Every generated exercise has its tests. Its solution runs before it is shown.
F1-L03-E013 tests
114 courses, 583 lessons, 1,801 exercises
The catalogue is the source of truth. It is written in French and is being translated into English; it covers 22 domains and holds about 275 hours of content.
- Introduction to Machine Learning (scikit-learn)
- Supervised Learning: Regression and Classification
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Model Evaluation and Validation
- Feature Engineering
- Time Series and Forecasting
- Deep Learning with PyTorch
- Natural Language Processing (NLP) and LLMs
- Deploying a Model (API, MLOps Basics)
14 tracks towards a job
Each track sets the base order of the courses. Yours starts there, then adapts to your successes and mistakes.
Data analyst
- Programming Basics and Algorithms
- Coding with an AI Assistant (Prompts, Code Review)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Descriptive Statistics
- Python Fundamentals
- Data Analysis with pandas and NumPy
- Data Cleaning and Preparation
- Visualization (Matplotlib, Seaborn, Plotly)
- Probability and Inferential Statistics
- Exploratory Data Analysis
- Business Metrics and KPIs
- Advanced SQL (CTEs, window functions)
- Data Storytelling and Presenting Results
- Automate with Python (Files, Excel, APIs)
- A/B Testing and Experimentation
- Simple ETL Pipelines
- Foundations
- SQL
- Statistics
- Python
- Data analysis
Data scientist
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Advanced SQL (CTEs, window functions)
- Descriptive Statistics
- Probability and Inferential Statistics
- Python Fundamentals
- Intermediate Python (Functions, Modules, OOP)
- Data Analysis with pandas and NumPy
- Data Cleaning and Preparation
- Visualization (Matplotlib, Seaborn, Plotly)
- Exploratory Data Analysis
- Introduction to Machine Learning (scikit-learn)
- Supervised Learning: Regression and Classification
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Model Evaluation and Validation
- Feature Engineering
- Time Series and Forecasting
- HTTP and REST APIs
- APIs with FastAPI
- Deep Learning with PyTorch
- Natural Language Processing (NLP) and LLMs
- Deploying a Model (API, MLOps Basics)
- Docker and Deployment
- Foundations
- SQL
- Statistics
- Python
- Data analysis
- Data science
- Back-end
- Python for the web
Power BI analyst
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- Descriptive Statistics
- Business Metrics and KPIs
- Getting Started with Power BI Desktop
- Power Query: Import and Transform
- Star Schema Modeling
- DAX Fundamentals
- Advanced DAX (Context, Time Intelligence)
- Data Storytelling and Presenting Results
- Designing Reports and Dashboards
- Power BI Service: Publishing, Sharing and RLS Security
- SQL
- Statistics
- Data analysis
- Power BI
JavaScript web developer
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- HTML Fundamentals
- CSS Fundamentals
- Responsive Layout (Flexbox, Grid)
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- Accessibility and SEO
- Data Structures and Complexity
- TypeScript
- React
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- HTTP and REST APIs
- Node.js Fundamentals
- API with Express
- Automated Testing
- Authentication and Web Security (OWASP)
- Full-Stack React + Node Application
- Docker and Deployment
- Foundations
- Front-end
- SQL
- Back-end
- Server-side JavaScript
Python web developer
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- Python Fundamentals
- Intermediate Python (Functions, Modules, OOP)
- HTML Fundamentals
- CSS Fundamentals
- Responsive Layout (Flexbox, Grid)
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- HTTP and REST APIs
- APIs with FastAPI
- Django Fundamentals
- Automated Testing
- Authentication and Web Security (OWASP)
- Complete Application with Django
- Docker and Deployment
- Foundations
- Python
- Front-end
- SQL
- Back-end
- Python for the web
PHP web developer
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- HTML Fundamentals
- CSS Fundamentals
- Responsive Layout (Flexbox, Grid)
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- PHP Fundamentals
- Object-Oriented PHP and Composer
- PHP and Databases (PDO)
- HTTP and REST APIs
- Laravel Fundamentals
- Automated Testing
- Authentication and Web Security (OWASP)
- Advanced Laravel (API, Queues, Tests)
- Docker and Deployment
- Foundations
- Front-end
- SQL
- PHP
- Back-end
Java web developer
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- Data Structures and Complexity
- HTML Fundamentals
- CSS Fundamentals
- Responsive Layout (Flexbox, Grid)
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- Java Fundamentals
- Object-Oriented Java
- Collections, Streams, and Exceptions
- Testing with JUnit
- HTTP and REST APIs
- Spring Boot: REST API
- Persistence with JPA and Hibernate
- Authentication and Web Security (OWASP)
- Docker and Deployment
- Foundations
- Front-end
- SQL
- Java
- Back-end
AI developer
- Programming Basics and Algorithms
- Python Fundamentals
- Intermediate Python (Functions, Modules, OOP)
- Git and GitHub
- Using Generative AI at Work
- HTTP and REST APIs
- Integrating an LLM into an Application
- RAG in Production
- AI Agents and Automation
- Evaluating and Monitoring an AI System
- Docker and Deployment
- Self-Hosting AI Models Locally
- Foundations
- Python
- Generative AI
- Back-end
Data engineer
- Terminal and Command Line
- Git and GitHub
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Advanced SQL (CTEs, window functions)
- Database Modeling
- Python Fundamentals
- Intermediate Python (Functions, Modules, OOP)
- Data Analysis with pandas and NumPy
- Data Warehouses and Analytical Modeling
- Data Quality and Governance
- Transforming Data with dbt
- Orchestrating Pipelines with Airflow
- Distributed Processing with Spark
- PostgreSQL in Practice (indexes, performance)
- Docker and Deployment
- Cloud Fundamentals
- Deploying an Application to the Cloud
- Foundations
- SQL
- Python
- Data engineering
- Back-end
- Cloud and DevOps
Marketing analyst
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Descriptive Statistics
- Business Metrics and KPIs
- Marketing Analytics
- E-commerce Analytics
- Data Storytelling and Presenting Results
- A/B Testing and Experimentation
- Local SEO and Online Presence
- SQL
- Statistics
- Data analysis
- Business analytics
- Websites and CMS
Cloud and DevOps engineer
- Terminal and Command Line
- Git and GitHub
- Linux for Developers
- Docker and Deployment
- Continuous Integration and Deployment
- Cloud Fundamentals
- Deploying an Application to the Cloud
- Kubernetes
- Infrastructure as Code with Terraform
- Cybersecurity: The Basics for Everyone
- Foundations
- Cloud and DevOps
- Back-end
- Cross-functional skills
Mobile developer
- Programming Basics and Algorithms
- Git and GitHub
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- TypeScript
- React
- HTTP and REST APIs
- Mobile Apps with React Native
- Mobile Apps with Flutter
- Foundations
- Front-end
- Back-end
- Mobile
Digital project manager
- Using Generative AI at Work
- Agile Project Management
- Business Analysis: From Needs to Specifications
- UX Design and Mockups
- GDPR for Data and Product Teams
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Business Metrics and KPIs
- Generative AI
- Cross-functional skills
- SQL
- Data analysis
Excel and reporting expert
- Excel Fundamentals
- Excel: Lookup and Logical Functions
- Excel: PivotTables
- Build and Safeguard a Reliable Excel Workbook
- Using Generative AI at Work
- Power Query and Power Pivot in Excel
- Automating Excel (VBA and Office Scripts)
- Power Automate: Automate Your Tasks
- Excel
- Generative AI
- Power Platform
What the catalogue lacks, we say so
When your goal goes beyond the catalogue, the platform offers the part it covers, names what is missing and records your request instead of inventing a path.
- Full coverage
- the path is generated as usual.
- Partial coverage
- the covered part is offered, the rest is named.
- No coverage
- your request is recorded and nearby tracks are suggested.
Your goal
Analyser les ventes de ma boutique avec Python et Spark
Nous pouvons t'apprendre Python et l'analyse de données. Nous n'avons pas encore de cours sur Spark : ta demande est enregistrée.
- Missing
- SparkRequest recorded
For schools and companies
A dedicated space is planned after the beta: learners and groups, assigned tracks with deadlines, progress tracking and requests for custom projects.
- Invite by email, CSV import or an allowed email domain
- Catalogue tracks, required or recommended
- Progress per learner and per group, blocking concepts
- Managers never see conversations with the tutor
Your path starts with your goal
Describe what you want to be able to do. The proposed path only contains lessons from the catalogue.