Overview
The MSc Business Analytics at ALU 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.
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. ALU 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
Faculty spotlight
Dr. Raj Patel leads cloud analytics architecture; Professor Margaret Ashford supervises capstone engagements with emphasis on executive communication and governance.
How you can study
Digital onlyStudy online
100% tuition-free online
Every programme can be completed fully online with zero tuition fees — study from anywhere in the world.
- ✦ Tuition: £0 / $0
- ✦ Course exams from $60 — 95% sponsorship rate
- ✦ Learn from anywhere · Live online seminars
Apply online → ◆◆
What You Will Learn
- ✦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
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Curriculum
Term 1 — Analytical Foundations
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| Code | Course | Credits |
|---|
| MSBA501 | Business Analytics Foundations & Decision Science Analytics maturity, decision quality, problem framing, and responsible analytics for executives. - Descriptive to prescriptive analytics
- Cognitive bias and decision science
- Analytics charter and stakeholder management
- Ethics and professional standards
| 3 |
| MSBA502 | Statistical Methods for Business Analytics Inference, regression, diagnostics, and A/B testing for commercial analysts. - Probability and uncertainty communication
- Linear & logistic regression interpretation
- Hypothesis testing and experiment design
- Executive memo writing from model output
| 3 |
| MSBA503 | Data Management, SQL & Analytics Engineering Dimensional modelling, SQL analytics, data quality, and modern transformation workflows. - Star schemas and KPI definitions
- Advanced SQL & window functions
- Data profiling and cleansing
- Analytics engineering & dbt concepts
| 3 |
| MSBA504 | Data Visualization & Executive Storytelling Dashboard design, visual perception, accessibility, and pyramid-principle narratives. - Chart selection and anti-patterns
- Power BI / Tableau professional workflow
- Executive dashboard wireframing
- Storytelling for board audiences
| 3 |
Term 2 — Advanced Analytics & Applications
▼
| Code | Course | Credits |
|---|
| MSBA505 | Predictive Analytics & Machine Learning for Business Supervised learning lifecycle, validation, feature engineering, and ML operations. - Baseline vs advanced models
- Cross-validation and leakage prevention
- Imbalanced classification & fraud use cases
- Model monitoring and drift
| 3 |
| MSBA506 | Optimization & Prescriptive Analytics Linear programming, simulation, pricing, and capacity planning under constraints. - LP formulation for business problems
- Monte Carlo simulation
- Revenue and supply chain optimisation
- Sensitivity analysis for executives
| 3 |
| MSBA507 | Big Data, Cloud Analytics & Modern Data Platforms Lakehouse architecture, distributed processing, streaming, FinOps, and platform security. - Cloud warehouse comparison
- Spark and batch processing
- Real-time analytics patterns
- Reference architecture design
| 3 |
| MSBA508 | Marketing, Customer & Digital Analytics Funnels, cohorts, CLV, attribution literacy, and privacy-first digital measurement. - Growth metrics and experimentation
- Customer lifetime value models
- Attribution limitations
- Personalisation and recommendations
| 3 |
| MSBA509 | Financial Analytics, Forecasting & Risk Modeling Driver-based planning, time series forecasting, and model risk management. - FP&A driver trees
- Forecast accuracy metrics
- Scenario and stress testing
- Model governance in finance
| 3 |
Term 3 — Strategy & Capstone
▼
| Code | Course | Credits |
|---|
| MSBA601 | Analytics Strategy, Ethics, Governance & AI Policy Analytics CoE design, maturity roadmaps, GDPR/AI Act literacy, and fairness testing. - Centralised vs federated operating models
- Analytics product management
- AI governance charters
- Building the business case for analytics
| 3 |
| MSBA602 | MSc Business Analytics Capstone Project Sponsored consulting engagement: data pipeline, modelling, recommendations, and executive defence. - Client proposal and sponsor management
- Reproducible analysis repository
- Interim and final recommendation packs
- Industry panel presentation
| 6 |
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Career Paths
- Business Analytics Manager
- Head of Insights / BI
- Management Consultant (Analytics practice)
- Product Analytics Lead
- FP&A & Revenue Analytics Director
- Chief Data Officer (CDO) track
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Programme Highlights
- ✦11 modules including industry-sponsored capstone with executive presentation
- ✦Pathway from ALU Academy analytics prep courses (SQL, BI, fundamentals)
- ✦Faculty with fintech, retail, and public-sector analytics leadership experience
- ✦Cloud analytics architecture module (Snowflake, BigQuery, Databricks concepts)
- ✦Ethics & AI governance module for regulated and multinational employers
- ✦Fully online delivery — no campus attendance required
- ✦Direct pathway from ALU Academy business analytics and SQL open courses
- ✦Analytics CoE and AI policy module for future CDO and Head of Insights roles
- ✦Sponsored capstone with reproducibility and sponsor confidentiality protocols
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Admission Requirements
- Bachelor's degree from an accredited institution (minimum 2:2 or GPA 2.5/4.0)
- Statement of Purpose (500 words minimum) outlining your goals and fit for the programme
- Two academic or professional references with contact details
- English proficiency: IELTS 5.5, TOEFL 46, or equivalent (waived for prior English-medium study)
- CV or résumé documenting relevant education and professional experience
