Education

Advanced study of governance operating models, data architecture, quality frameworks, and metadata/lineage management.

• Produced strategy documents and governance playbooks mapped to DAMA-DMBOK2 functions.
• Applied GDPR and Kenya DPA principles to data lifecycle design and AI use-case risk reviews.
• Explored research methods, survey design, and field data collection techniques.

Focused on the technical and organizational controls needed to secure AI pipelines and enforce responsible-use policies.

• Applied risk assessment models for AI systems in production.
• Built guardrail frameworks covering data privacy, bias, and auditability.

Certified in data management practices aligned with DAMA-DMBOK2.

• Developed frameworks for data governance, stewardship, and compliance.
• Applied methods for data quality, metadata, and master/reference data management.
• Covered topics on data architecture, integration, and warehousing design.

An executive-level programme covering how to govern, deploy, and strategically position AI to create sustainable value while managing risk.

• Explored AI business strategy and organizational change management.
• Covered AI ethics, policy frameworks, and regulatory compliance considerations.

Hands-on projects across supervised and unsupervised learning, model evaluation, and deployment.

• Applied data wrangling, feature engineering, and experimentation techniques.
• Built and deployed machine learning pipelines with attention to scalability.
• Capstone: Disaster Response Pipeline — multilabel NLP classification.

SQL, statistical inference, exploratory data analysis (EDA), and visualization.

• Applied statistical methods to evaluate hypotheses and uncover trends.
• Projects included OpenStreetMap data wrangling and weather trends analysis.

Database systems, networking, software engineering, and IT governance.

• Applied SDLC best practices in individual and group projects.
• Final project: developed a real-time bidding application using Django/SQLite.