In this course, learners will build a rigorous and practical foundation in data literacy, statistics, and the mathematical principles underpinning modern data science. Learners will explore data collection, types, quality, and visualisation, and apply statistical thinking and prompt engineering to simplify and communicate data insights. They will develop a thorough grounding in descriptive statistics and probability, including sampling, probability distributions, and Python-based statistical analysis. Learners will apply inferential statistics through hypothesis testing — covering t-tests, binomial tests, association tests, and significance thresholds – before exploring the linear algebra and calculus concepts essential for data science, including matrix decomposition, eigendecomposition, differential calculus, derivatives, and integration. They will build and evaluate regression models using linear and logistic regression, gradient descent, and scikit-learn, and apply exploratory data analysis techniques. The course also covers data management and governance through DAMA-DMBOK frameworks, data quality, pipelines, wrangling, and emerging trends including data fabric, observability, and AI TRiSM, before concluding with a leadership-focused module on using data for strategic decision-making, KPI frameworks, and data monetisation.