What you’ll learn
Guiding you through a curated curriculum designed to take you from “just curious” to “confident” in Data Science & AI—learning how to work with data and build machine learning models in 12 weeks full-time or 24 weeks part-time (live online, Paris time).
Foundation
SQL, Python, Jupyter Notebook, Git and GitHub, Linear Algebra, Probability and Statistics.
Data Analytics
Data Analysis, Data Preparation, Data Visualisation and Data Exploration.
Classic Machine Learning
Machine Learning, supervised and unsupervised learning, model improvement, Naive Bayes, SVM, Random Forests, ML Pipelines and Classification.
Deep Learning
Neural Networks (implementation, troubleshooting & optimisation), CNN architectures, Autoencoders, Data Augmentation, TensorFlow, Keras and Scikit-Learn.
Natural Language Processing
Text processing for NLP, RNN, LSTM, attention mechanisms, transformer models and chatbot building.
Chapter 0: Pre-Work
Data Science & AI are increasingly central to how organisations make decisions and build products. In this chapter, you’ll cover foundations—Python, SQL, maths, and version control—so you’re ready to work with data confidently and begin the machine learning workflow.
Introduction to Python
- Python Language And History
- The Basics Of Python
- Fundamental Data Structures In Python
- Classes And Objects
- Modules And Packages
- Input/Output
- Errors And Exceptions
Environments
- Python Environments
- Anaconda
- Jupyter Notebooks
SQL and Databases
- SQL Fundamentals
- SQL Queries
Linear Algebra
- Scalars And Vectors
- Matrices
- Norms
Git and GitHub
- Introduction to Version Control
- Workflow
- Inspecting Repositories
- Undoing Changes
- Fetching And Pulling Changes
- Pushing Changes
Project: Curve Fitting
- This project is about solving the 'Curve fitting' problem, which involves finding the best curve equation to fit a given dataset. It will guide you through an example of this problem and is divided into sections, where each section will exercise the use of fundamental concepts such as OOP, SQL, Linear Algebra, and the final Machine learning workflow.
Learning Schedule (Paris time)
Monday to Friday, 09:30–15:30
Lecture Session
09:30 - 11:00
Lecture Session
11:30 - 13:00
Hands-on Session
14:00 - 15:30
Our Methodology
Online Live Learning
- Join interactive live sessions run on Paris time (CET/CEST). You’ll learn with instructors and peers in real time—asking questions, practising together, and building momentum week by week.
Self Study
- Part-time: 9 hours live learning + ~11 hours self-study (20 hours per week)
- Full-time: 22.5 hours live learning + ~17.5 hours self-study (40 hours per week)
Flipped Classroom Method
- Learn efficiently with a flipped classroom approach: prepare with focused material, then use live sessions for discussion, practice, and feedback—so you actually apply what you learn.
Guided Practice
- Build confidence through guided exercises, hands-on labs, and portfolio projects. You’ll practise the workflows used in real teams—not just watch content.
Prefer to learn at your own pace?
Learn with our On-demand Data Science & AI course at your own pace with complete flexibility through our immersive, self-paced program. Master how to transform raw data into powerful insights, train intelligent models, and build real-world AI applications and graduate ready to launch your career in the world of data-driven innovation.
What You’ll Need
You don’t need prior qualifications in computer science to join. We guide you through the essentials early on, with structured pre-work and mentoring to build confidence. Whether you’re learning data skills for your current job or building a stronger technical foundation, the programme is designed to help you progress step by step—online from France.
- Laptop or Computer: A reliable laptop or desktop computer with sufficient processing power and capable of running necessary programs.
- Stable Internet Connection: Reliable internet access for live learning sessions, hands-on labs, and assignments.
- Basic Computer Literacy: Ability to navigate operating systems, productivity software, and internet browsers.
- Comfort with logic and basic maths: No prior coding is required, but you should be comfortable with problem-solving and ready to practise programming during the pre-work to establish a strong foundation.
- English Proficiency: B1 level with the ability to understand technical materials and communicate effectively, as the bootcamp is conducted in English.
- Commitment to Learning: A proactive attitude toward learning and practising—especially during the pre-unit to establish a solid foundation for continuing in the bootcamp.
Career Services Center
Career development workshops
Open sessions for learners and the wider CLA community. Get practical guidance on CV structure, LinkedIn, job-search strategy, and how to talk about your projects—plus last-minute polish before an application.
1:1 career guidance sessions
Structured sessions to clarify your direction and build a realistic action plan—whether you’re aiming for a new role, a promotion, or adding tech skills to your current job.
Mock interviews
Practise common interview formats: behavioural questions, technical discussions, and case-style conversations. Learn how to present your strengths, talk through trade-offs, and navigate salary expectations with confidence.
CV & cover letter reviews
Get actionable feedback on your CV and cover letter, tailored to roles in France and Europe—so recruiters can quickly understand your skills, projects, and impact.
Job & internship round-up
A curated view of opportunities shared by our career team, with an emphasis on junior, entry-level, and “junior-friendly” roles—plus roles where upskilling is valued.
Career resources platform access
Ongoing access to career materials, templates, assignments, and tech-industry resources inside our learning platform.
Professional guidance & networking events
Meet people working in tech and digital roles to get practical advice on skills, hiring processes, and day-to-day work. Join career chats to learn how different roles operate in real companies.
Alumni networking
Stay connected with classmates and alumni in France and beyond—share opportunities, recommend resources, and exchange feedback on portfolios and interviews.
Why Choose Code Labs Academy?
1-to-1 Career Coaching
Personalised support from career specialists: CV and LinkedIn refresh, mock interviews, and a tech-focused job-search strategy.
Portfolio-Ready Projects
Graduate with a GitHub-ready portfolio of real-world projects—built in class and polished with mentor feedback.
Industry-Driven Curriculum
Curriculum refreshed every quarter to match current hiring needs in AI, cybersecurity, and web development.
Recognised Certificate
Showcase your AZAV-accredited Code Labs Academy certificate on LinkedIn, your CV, and visa applications.