Diploma in Applied AI, ML Engineering & Data Science

Course Structure

• Duration: 3 Months Onsite | Full Internship included

• Onsite Weekdays: Mondays to Fridays 10pm - 4.00pm  (GMT+1)

• Access to the Catalyst AI Hub Africa Alumni Network

• 30 days of post-programme support

  14 Hands-On Computer Vision Labs

• Full Internship to Building Real AI Application 

• Full Support for Start-up and VC Support Incuded

• 60+ structured lab sessions across 6 modules

• 10+ portfolio-ready projects across 6 modules

• Labs: 10 Hands-On Dashboard Labs

• Integrated final project combining CV, NLP, and data visualisation

• Theory vs Practical: 15% Theory  |  85% Hands-On Practical & Project Work

• No prior coding experience required for enrolment — the programme starts from zero

Ideal For

This course is designed for:

• University Students  

• Recent Graduates (0–2 years out)

• Young Secondary School Levers 

• Career Changers from Non-Tech Fields

• Entrepreneurs and Founders

• Nigerian Diaspora Returnees

• Complete beginners to programming — Python Foundations modules included from scratch

Recommended prior knowledge: The Diploma in AI Engineering & Data Science is designed for a specific profile of learner: someone who is ambitious, willing to commit to an intensive three-month programme, and ready to invest serious effort in building a skill set that will differentiate them in the Nigerian and African job market for the next decade.

Course Description 

The Diploma in AI Engineering & Data Science is a rigorous, immersive three-month programme designed to take participants from the foundational principles of Artificial Intelligence through to the advanced applied techniques that are driving the most impactful technology deployments in Nigeria and across Africa. It is the only structured diploma-level AI engineering programme on the continent that combines AI literacy, data analytics, Python programming, data visualisation, computer vision, and natural language processing in a single integrated curriculum.

 The programme is deliberately structured as a progression: each module builds on the one before it. Participants who begin with no coding experience finish three months later having built a computer vision object detection system and a natural language processing pipeline — both running in production and both portfolio-ready for job applications and client presentations.

Summary of Learning  

By the end of this course, participants will be able to:

• 1.   DEVELOP AI LITERACY AND STRATEGIC AWARENESS — Graduate with the ability to accurately classify AI deployments, articulate their business value, design AI workflows for non-technical stakeholders, and evaluate AI opportunities critically. No longer a passive consumer of AI technology; an informed architect of its application.

• 2.   BUILD DATA ANALYSIS AND VISUALISATION PROFICIENCY — Master Excel, Power BI, and Python-based data analysis from raw data ingestion through to boardroom-ready dashboards and executive data stories. Understand what data is saying and communicate it compellingly to decision-makers.

• 3.   ACQUIRE PRODUCTION-GRADE PYTHON SKILLS FOR AI — Write Python code that is clean, documented, reproducible, and deployable. Use the core Python AI and data science libraries (NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Plotly) with professional competence.

• 4.   APPLY COMPUTER VISION TECHNIQUES TO REAL PROBLEMS — Build computer vision systems using OpenCV and deep learning that can detect objects, classify images, and process video streams. Understand the architecture behind state-of-the-art vision models and deploy them in production environments.

• 5.   BUILD NATURAL LANGUAGE PROCESSING PIPELINES — Process, analyse, and derive intelligence from text data using classical NLP methods and transformer-based models. Build systems that understand customer sentiment, extract key information from documents, classify text, and generate intelligent responses.

• 6.   PRODUCE A PROFESSIONAL AI ENGINEERING PORTFOLIO — Graduate with a portfolio of 10+ documented, deployable AI and data projects that demonstrate real capability to employers, clients, and investors — not certificates of participation, but working systems that solve real problems.