MSc · Postgraduate
Data Science & Applied Analytics
Turn complex data into credible insight, useful models and better decisions.
- Award
- MSc
- Indicative duration
- 18 months part-time
- Delivery
- Online
- Academic school
- Computing
A deeper understanding.
A practical direction.
Combine statistics, computing and domain understanding to work through the complete analytical lifecycle. Learn to question data quality, communicate uncertainty and assess how models behave beyond the classroom.
What you will learn
- Build reproducible data pipelines and analytical workflows.
- Select and evaluate statistical and machine-learning methods.
- Communicate uncertainty, assumptions and model limitations.
- Apply responsible data governance to organisational problems.
Your learning journey
The indicative curriculum builds from core understanding to independent work. Select a stage to explore its subjects.
Stage 1 · Analytical foundations
- Statistical reasoning
- Python for analytics
- Data management
- Research methods
Stage 2 · Applied methods
- Machine learning
- Data visualisation
- Time series & forecasting
- Responsible data practice
Stage 3 · Independent inquiry
- Applied analytics clinic
- Model deployment & monitoring
- Research dissertation / industry capstone
PIEM in your programme
An industry-connected challenge
Develop and evaluate a demand-forecasting model, comparing it with a simple baseline and reporting the operational consequences of prediction errors.
Applied Analytics Clinic
A supervised analytics service that helps organisations frame questions, assess data quality and test decision-support tools.
Partner ecosystem
The programme’s partnership network is designed to include:
- Data-led businesses
- Research institutes
- Public-sector analytical teams
- Technology service providers
Partner involvement can include project briefs, expert critique, practice insights and mentoring. Specific organisations and opportunities are confirmed for each cohort.
Understand the PIEM modelWhere your learning can lead
These are potential areas of work, not guaranteed outcomes. Career development combines academic learning, demonstrable project work and individual professional preparation.
Plan your study.
Use these details to compare your options. Confirm the approved specification before making a commitment.
- Next intake
- Intake dates to be confirmed
- Fees & funding
- Fee schedule awaiting approval. Explore cost planning.
- Study commitment
- Part-time pattern; weekly workload to be confirmed. Allow time for taught sessions, independent work, group projects and assessment.
- Practical requirements
- Online projects and live collaboration; confirm any assessment, fieldwork or specialist access requirements before enrolment.
- Your support
- Academic feedback, study guidance and accessibility support arrangements to be confirmed for the cohort. Explore support.
Learning made visible
Follow a project from question to contribution.
See the decisions, outputs and reflection behind an illustrative PIEM experience connected to this field.
Explore a project conceptEntry, delivery & funding
Entry expectations
A recognised bachelor’s qualification in a relevant field, or a demonstrably equivalent background. Evidence of quantitative capability and introductory programming is expected.
Applicants whose previous education was not in English may need to demonstrate readiness to study in English. Country equivalence, evidence and any additional requirements are reviewed individually.
How you will study
Structured online study combines guided independent learning, facilitated discussion, scheduled live sessions and project feedback.
Assessment
Assessment may include analytical assignments, project work, presentations, practical demonstrations and an independent capstone. Clear criteria and feedback support your progress.