MSc Data Science

Overview

Data science sits at the intersection of statistics, computing, and domain expertise. This programme teaches you to frame business questions as analytical problems, wrangle messy real-world datasets, and communicate findings to non-technical audiences. Modules cover Bayesian inference, deep learning, NLP, and MLOps. You will work with public datasets from healthcare, finance, and climate science, building a portfolio that demonstrates end-to-end analytical capability. MSc Data Science is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 36. Students move from Statistics & Probability for Data Science through Statistics & Probability for Data Science; Programming for Analytics (Python/R); Data Wrangling & SQL; Data Visualisation & Storytelling; Machine Learning Fundamentals; Deep Learning & Neural Networks, and finish on MSc Data Science Capstone. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Apply statistical inference and experimental design to business problems Build and validate predictive models using scikit-learn, PyTorch, and R Design dashboards and narratives that drive executive action Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.

Every module uses real datasets — never toy CSVs alone. You will document reproducible notebooks and present to a mock executive panel in the capstone. AFU partners with public-health NGOs for anonymised analytics projects.

Who This Programme Is For

• ✦Analysts upgrading from Excel to Python and ML workflows
• ✦Engineers moving into ML engineering or analytics engineering
• ✦Researchers needing formal statistical and computing credentials

Programme Structure & Delivery

Learning Outcomes

• ✦Apply statistical inference and experimental design to business problems
• ✦Build and validate predictive models using scikit-learn, PyTorch, and R
• ✦Design dashboards and narratives that drive executive action
• ✦Implement reproducible data pipelines with version control and testing
• ✦Evaluate AI systems for fairness, bias, and regulatory compliance
• ✦Design A/B tests and interpret results for product and policy teams
• ✦Document model cards and monitoring plans for production ML

Career Pathways

• ✦Data Scientist
• ✦Analytics Engineer
• ✦Business Intelligence Lead
• ✦Quantitative Research Analyst
• ✦AI Product Manager

Curriculum