It's not just about using AI.
It's about building world class products with it.

Prompting, automation, agents, and applied AI products. You leave able to ship a working assistant, not just talk about one.

• Prompt engineering and model evaluation across frontier models
• Automations that remove real hours of work using AI agents
• Build and deploy an AI assistant end to end with memory and APIs
• Retrieval-Augmented Generation and vector search for real business data
• Fine-tuning, evals and responsible AI guardrails
• Ship a portfolio product that solves a Nigerian problem
• Halal-conscious, ethical use of AI at work and in products

Analytics and the pipelines behind it. You learn to model, clean and move data at scale, then turn it into dashboards and decisions a business can act on.

• SQL to an advanced standard: joins, windows, CTEs, query tuning
• Python for analysis: pandas, NumPy, visualisation and statistics
• Data engineering: warehouse modelling, ELT pipelines, dbt and Airflow orchestration
• Cloud storage and warehousing plus data quality, testing and documentation
• BI dashboards leadership actually reads, and an intro to practical machine learning
• A capstone: an end to end pipeline and dashboard on a real Nigerian dataset

A modern platform engineering path: Linux and networking foundations, containers, Kubernetes, infrastructure as code, CI/CD and observability, finished with a production grade deployment.

• Linux, shell scripting, networking and Git workflows
• Docker containers, image security and Kubernetes deployment on the cloud
• Infrastructure as code with Terraform and configuration management with Ansible
• CI/CD pipelines with GitHub Actions, plus GitOps delivery using Argo CD
• Observability: Prometheus, Grafana, structured logging, alerting and incident response
• Cloud and DevSecOps essentials: IAM, secrets management, cost control and reliability (SLOs)
• A capstone: a containerised app deployed, automated, monitored and documented