What you’ll learn
We’ll guide you through a carefully curated curriculum designed to take you from ‘just curious’ to confident in Data Science & AI 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 and AI have become some of the most in-demand skill sets in recent years. This field involves handling data, cleaning it, analysing it, and developing machine learning models to predict outcomes. 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 9:30am–3:30pm (NZT/NZDT)
Lecture Session
9:30am - 11:00am
Lecture Session
11:30am - 01:00pm
Hands-on Session
02:00pm - 03:30pm
Our methodology
Online live learning
- Here at CLA, elevate your learning experience with our interactive online live sessions, where you'll engage directly with instructors and peers in real-time, fostering collaboration and rapid skill development.
Self-study
- Part-time: 9 hours live learning, 11 hours of self-study (20 hours per week)
- Full-time: 22.5 hours live learning, 17.5 hours of self-study (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 at our bootcamp, 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 any prior qualifications in computer science or programming to join our bootcamp. We assume no prior knowledge and will guide you through the basics in the first few weeks, ensuring you build a strong foundation from the ground up. Whether you're starting from scratch or upskilling for your current role (or your next opportunity), our 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: Familiarity with programming concepts to follow the courses comfortably.
- 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 practicing coding, especially during the pre-unit to establish a solid foundation for continuing in the bootcamp.
Career Services Centre
Career development workshops
Short sessions for you and the wider CLA community. Get a feel for what our Career Services Centre offers, meet our careers adviser, and drop in for a last-minute CV tidy-up.
Personalised career coaching (1:1)
Structured sessions to clarify your goals and build a plan—whether you’re aiming for a new role, upskilling for your current job, or moving into tech over time.
Mock interviews
Practise common behavioural and technical interview questions, learn how to highlight your strengths, and get support with salary discussions and negotiation.
CV and cover letter reviews
Get personalised recommendations on your CV and cover letter, tailored to help you stand out and land interviews.
Job and internship round-up
Regular updates on new opportunities, curated by our career specialists and suitable for early-career and junior-level roles.
Career resources platform access
Access to career materials, assignments, and tech industry resources through our learning platform.
Professional guidance and networking sessions
Meet tech professionals, ask questions, and build connections through guest talks and Career Chat-style events.
Alumni networking
Connect with classmates and alumni to share resources, start discussions, and pass on job leads and opportunities.
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.