Are you passionate about technology but unsure where to begin? Curious about AI, automation, or data science but feel overwhelmed by the complexity?
This foundational technology training programme – developed by Alca.ai in partnership with Digital Jersey – is designed to empower individuals in Jersey with the skills needed to thrive in the digital economy. Whether you’re a student, career changer, or entrepreneur, this course will help you build confidence and competence in programming and modern AI systems.
Unlike traditional courses, this programme is built on the globally successful Stanford University “Code in Place” model. It prioritises Human-Centred Education, combining world-class content with real-world case studies, mentoring, and coaching to ensure a high-impact, low-dropout learning experience.
Course Structure & Study Commitment
This course is designed to support independent learning. You’ll be expected to commit around 5 hours per week to self-paced study, allowing you to work through the materials in your own time, at your own pace.
Start: Monday 15th September 2025
End: Thursday 13th November 2025
Course Structure & Study Commitment
This course is designed to support independent learning. You’ll be expected to commit around 5 hours per week to self-paced study, allowing you to work through the materials in your own time, at your own pace.
You’ll take part in a combination of weekly online sessions and in-person sessions held at the Digital Jersey Academy. The course includes 8 live sessions spread over 9 weeks, with a break scheduled for half-term.
Free
Key Features
By the end of this programme you will:
Syllabus Overview
Weeks 1-2: The Karel Sandbox
Learn programming logic using Karel the Robot – master loops, conditionals, and decomposition without worrying about syntax.
Weeks 3-4: The Console
Transition to Python. Learn variables, expressions, user input/output, and basic data types.
Week 5: Graphics
Create visual projects using functions and parameters. Build your own “Random Art” project.
Week 6: Data structures
Explore lists and dictionaries to manage complex data. Apply your skills in the “World’s Hardest Game” project.
Beyond Week 6: Final Project
Design and build your own application. Past examples include a “Computer Science Quiz App” and a “Phonebook” with data persistence.
Course Structure & Study Commitment
This course is designed to support independent learning. You’ll be expected to commit around 5 hours per week to self-paced study, allowing you to work through the materials in your own time, at your own pace.
Francois Chesnay
My journey into Artificial Intelligence began in the mid-80s till the late 90s, with a pause of 15 years. It reignited in 2015 when AI was used to play Atari games, demonstrating significant advancements in deep learning, and ultimately leading me to complete Stanford’s Artificial Intelligence Graduate Certificate Program. Prior to this, I earned an MBA from the University of Chicago-Booth, a Master’s from the London School of Economics and enjoyed a career in structured finance, venture capital, and corporate restructuring. As a course facilitator for XCS221 Artificial Intelligence: Principles and Techniques and XCS224N Natural Language Processing with Deep Learning at Stanford’s Engineering Center for Global & Online Education, I support global learners in AI. I also serve as a non-executive director, advise on AI and strategy, and mentor startup founders.
Gianfranco Bombardieri
I lead the Global Business Intelligence & Automation practice at Adaptive, a fintech consultancy, where I help C-suite executives transform complex data into actionable insights that drive smarter decision-making. Teaching is equally central to my work. For three consecutive years, I’ve had the joy of instructing Stanford’s Code in Place program, teaching introductory programming to students worldwide. This experience has deepened my conviction that the best learning happens when complex concepts become accessible and engaging.
My professional journey began in sports analytics, supporting UEFA and FIFA competitions, which revealed data’s transformative power through computer vision and creative analysis. I later contributed to the London 2012 Olympics and joined the International Olympic Committee in Lausanne, shaping strategy and data initiatives for the global sports industry.
I hold an MBA focused on quantitative decision-making methods and continue expanding my expertise in AI, machine learning, and neural networks.