MSc Business Analytics

Overview

The MSc Business Analytics at Durham is designed for ambitious analysts, product leaders, and consultants who need more than dashboards — you must frame decisions, build reproducible pipelines, deploy models responsibly, and communicate findings to executives under uncertainty. Unlike a pure data science degree, this programme emphasises **business impact**: marketing analytics, financial forecasting, prescriptive optimisation, and analytics governance aligned with UK GDPR and EU AI Act literacy. Each module combines faculty-led video sessions, case-based assignments, and tools used in enterprise analytics teams (SQL, Python, Power BI/Tableau, cloud warehouses). The capstone is a sponsored consulting engagement with a live brief, faculty mentorship, and industry panel review. MSc Business Analytics 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 Business Analytics Foundations & Decision Science through Business Analytics Foundations & Decision Science; Statistical Methods for Business Analytics; Data Management, SQL & Analytics Engineering; Data Visualization & Executive Storytelling; Predictive Analytics & Machine Learning for Business; Optimization & Prescriptive Analytics, and finish on MSc Business Analytics Capstone Project. 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: Frame complex business problems as structured analytics engagements with clear KPIs Apply statistical inference, regression, and experimentation to commercial decisions Engineer trustworthy SQL pipelines and dimensional models for BI and ML 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 is built for professionals who must influence decisions — not only build models. You will maintain a portfolio repository (SQL, notebooks, dashboards, governance artefacts) reviewed by faculty and presented to an industry panel in the capstone. AFU partners with retail and fintech sponsors for anonymised capstone datasets.

Who This Programme Is For

• ✦Business analysts upgrading to analytics leadership roles
• ✦Consultants specialising in data-driven strategy and operations
• ✦Product and marketing leaders owning experimentation and growth metrics
• ✦Finance and FP&A professionals adopting advanced forecasting methods

Programme Structure & Delivery

Learning Outcomes

• ✦Frame complex business problems as structured analytics engagements with clear KPIs
• ✦Apply statistical inference, regression, and experimentation to commercial decisions
• ✦Engineer trustworthy SQL pipelines and dimensional models for BI and ML
• ✦Build and validate predictive models with professional monitoring and documentation
• ✦Design analytics operating models, governance charters, and ethical AI policies
• ✦Deliver board-ready narratives, dashboards, and capstone recommendations
• ✦Present a capstone recommendation pack to a mock executive panel
• ✦Produce model cards and analytics governance documentation suitable for regulated employers

Career Pathways

• ✦Business Analytics Manager
• ✦Head of Insights / BI
• ✦Management Consultant (Analytics practice)
• ✦Product Analytics Lead
• ✦FP&A & Revenue Analytics Director
• ✦Chief Data Officer (CDO) track

Curriculum