EDUCATION & CREDENTIALS
My academic background combines data science, business management, computer science, and multidisciplinary scientific study. This supports my approach to building AI systems that connect technical implementation with business decision-making.
01 / ACADEMIC EDUCATION
Programme status and scope are presented conservatively, without unsupported grades, honours, rankings, or accreditation claims.
A postgraduate programme focused on applied data science, machine learning, Python programming, databases, data infrastructure, data visualisation, mathematics and statistics, spatial data science, ethics, governance, and research project management.
Postgraduate business education covering project management, business development, brand management, marketing analytics, market research, financial management, marketing strategy, customer relationship management, and business decision-making.
A multidisciplinary science degree combining computer science, chemistry, biochemistry, and zoology, with foundations in programming, scientific research, laboratory methods, quantitative reasoning, analytical thinking, and problem-solving.
02 / PROFESSIONAL CERTIFICATES
Selected IBM and Google professional certificates and specialisations covering AI engineering, machine learning, deep learning, Generative AI, RAG, agentic AI, data science, analytics, Python, and responsible AI.
RAG applications, vector databases, advanced retrieval, multimodal AI, function calling, LangChain, LangGraph, multi-agent workflows, MCP, memory, tools, and agentic AI system design.
Verify on Coursera ↗Generative AI architectures, LLMs, NLP, transformers, tokenisation, attention, prompt engineering, PyTorch, Hugging Face, LangChain, RAG, and model fine-tuning.
Verify on Coursera ↗Machine learning, deep learning, neural networks, PyTorch, TensorFlow, Keras, transformers, LLM engineering, NLP, LangChain, and RAG application development.
Verify on Coursera ↗Neural networks, CNNs, PyTorch, TensorFlow, Keras, NLP, computer vision, recommendation systems, transformers, time-series modelling, and deep-learning pipelines.
Verify on Coursera ↗Python, SQL, data-science methodology, data analysis, visualisation, machine learning, databases, cloud labs, and an applied capstone project.
Verify on Coursera ↗Software engineering foundations, Python, Flask, HTML, CSS, JavaScript, Generative AI applications, chatbots, prompt engineering, and AI-powered web development.
Verify on Coursera ↗03 / SELECTED COURSES
A focused selection of individual online courses supporting cloud, AI-platform, DevOps, and prompt-engineering practice. These courses are not presented as university degrees or professional licences.
AWS cloud foundations, core services, cloud architecture, security, networking, storage, and compute.
Verify Credential ↗Microsoft Azure AI services, machine learning capabilities, responsible AI, and cloud-based AI solutions.
Verify Credential ↗Google Cloud AI and machine learning services, ML workflows, cloud-based model development, and applied AI capabilities.
Verify Credential ↗DevOps culture, continuous integration, continuous delivery, automation, collaboration, testing, and deployment practices.
Verify Credential ↗Prompt patterns, structured instructions, prompt refinement, context design, and practical use of large language models.
Verify Credential ↗04 / ADDITIONAL CREDENTIALS