What you’ll learn
Guiding you through a carefully curated curriculum designed to take you from ‘just curious’ to confident in Data Science & AI, building models and deploying real projects in as few as 12 weeks (full-time) or 24 weeks (part-time).
Foundation
SQL, Python, Jupyter Notebook, Git and GitHub, Linear Algebra, Probabilities and Statistics.
Data Analytics
Data Analysis, Data Preparation, Data Visualization and Data Exploration.
Classic Machine Learning
Machine Learning, Supervised and Unsupervised learning, ML model enhancement, Naive Bayes, SVM, Random Forests, ML Pipelines and Classification.
Deep Learning
Neural Networks (implementation, troubleshooting & optimisation), CNN Architectures, Autoencoder Architecture, Data Augmentation, Tensorflow, Keras and Scikit-Learn.
Natural Language Processing
Text coding for NLP, Recurrent Neural Networks (RNN), LSTM, Attention Mechanisms, Transformer Model and chatbot building.
Chapter 0: Pre-Work
Data science has been one of the most prestigious careers in recent years. It involves handling data, cleaning it, evaluating it, and developing machine learning models to predict outcomes of events. In this chapter, we will cover the foundations of data science to get you ready to begin your learning journey.
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
Monday to Friday, 09:30–15:30 (CET/CEST)
Lecture session
09:30–11:00
Lecture session
11:30–13:00
Hands-on session
14:00–15:30
Our methodology
Online live learning
- Elevate your learning experience with interactive online live sessions, where you’ll engage directly with instructors and peers in real time, with schedules designed for Central European Time (CET/CEST).
Self-study
- Part-time: 9 hours live learning, 11 hours of self-study, total 20 hours per week
- Full-time: 22.5 hours live learning, 17.5 hours of self-study, total 40 hours per week
Flipped classroom method
- Experience a dynamic learning approach with our flipped classroom method, empowering you to learn actively and engage deeply in our bootcamp curriculum.
Guided practice
- Engage in an immersive learning experience where we focus on active participation, hands-on exercises, and portfolio building to ensure a solid foundation to your knowledge.
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 a computer science degree to join our bootcamp. We’ll guide you through key foundations and support you step by step, starting with pre-work that helps you build momentum before live classes begin. Whether you’re upskilling for your current role or working towards a data-focused role, the programme is designed to help you progress quickly and confidently.
- 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.
- Basic Knowledge in Algorithmics and Programming: Some familiarity with programming concepts helps you follow the course comfortably (we also cover key foundations in the pre-work).
- 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
Practical sessions for you and the wider CLA community. Get a taste of what our Career Center offers, meet our career advisors, and drop in for last-minute CV or LinkedIn polish.
Personalised career counselling sessions
Structured sessions to clarify your goals and build a realistic plan, from identifying target roles to setting next-step actions that fit your learning path.
Mock interviews
Practise common interview questions and learn how to communicate your strengths, talk through projects, and navigate salary discussions with confidence.
CV & cover letter reviews
Get personalised recommendations on your CV and cover letter, tailored to help recruiters quickly understand your skills and invite you to interview.
Job and internship round-up
New opportunities curated by our career specialists, with a focus on junior-friendly roles, internships, and entry-level pathways.
Career resources platform access
Ongoing access to career materials, assignments, templates, and tech industry resources inside our learning platform.
Professional guidance & networking events
Meet tech professionals for advice, perspective, and networking, and connect with other learners during Career Chat events.
Alumni networking
Connect with classmates and alumni to share resources, discuss industry topics, and exchange job openings that may be relevant to the community.
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.