My certificates.

Covered the core building blocks of data science including data wrangling, exploratory analysis, and introductory machine learning. Worked with real datasets to clean, transform, and summarise data using R and Python. Developed a foundational understanding of statistical thinking and how to communicate findings to both technical and non-technical audiences.

Learned to leverage Claude Code for AI-assisted software development, from writing and debugging code to automating complex multi-step workflows. Explored how to integrate AI into the development cycle through clear prompting, iterative refinement, and tool orchestration. Gained hands-on experience building intelligent pipelines that accelerate delivery and reduce manual effort.

Explored the fundamentals of artificial intelligence including supervised and unsupervised learning, neural networks, and large language models. Examined how AI is applied across industries — from healthcare and finance to automation and customer service. Gained an understanding of responsible AI practices, model limitations, and the ethical considerations shaping modern AI development.

Gained a comprehensive understanding of what data science is, the tools it involves, and the role of a data scientist in a modern organisation. Explored the end-to-end workflow — from problem definition and data collection to analysis, modelling, and storytelling. Learned how data science intersects with business strategy, AI, and decision-making to create measurable impact.

Gained hands-on experience with the most widely used tools in data science — Jupyter Notebooks, RStudio, GitHub, and IBM Watson Studio. Learned how to set up development environments, version-control projects, and collaborate on data-driven work. Applied R and Python in structured, notebook-based workflows to solve real data problems and present reproducible results.

Learned the structured, step-by-step methodology data scientists use to move from a business question to a deployed solution. Covered each stage of the workflow — business and analytic understanding, data requirements and collection, data preparation, modelling, evaluation, deployment, and feedback. Practised framing problems clearly and choosing the right analytical approach so that models genuinely answer the question being asked.