Year one
Year one provides apprentices with a complete, integrated grounding in data analytics, developing the knowledge, skills and professional judgement needed to work effectively with data in modern organisations.
Across the year, apprentices develop their capability not only to use tools and techniques, but to understand why methods work, when they should be applied, and how insights should be communicated to a range of stakeholders. Learning progresses from foundational concepts through to applied analytical practice, building confidence in working with data across end鈥憈o鈥慹nd workflows.
By the end of the year, apprentices can contribute effectively to analytics activity that supports informed decision鈥憁aking, performance improvement and strategic insight.
Developing practical and transferable data skills
A key focus is enabling apprentices to work fluently with real鈥憌orld data and develop practical skills that transfer directly across roles and contexts.
Apprentices build strong programming capability for data handling and analysis, allowing them to:
- Write reliable, reusable code to load, clean and structure datasets
- Explore and analyse data efficiently and consistently
- Reduce manual effort and improve the quality of everyday data tasks
- Contribute confidently to data鈥慹nabled workflows and projects
Alongside programming, apprentices develop the statistical and mathematical understanding required to interpret data appropriately and justify analytical decisions. Rather than applying techniques as "black boxes", they gain insight into how core concepts underpin common data methods, including:
- Probability and statistical inference
- Linear algebra concepts used in modelling
- Calculus principles that support optimisation and analytical techniques
This deeper understanding strengthens analytical reasoning, improves judgement in method selection and enhances the credibility of analytical outputs.
Strengthening analytical thinking and decision鈥憁aking
Throughout the year, apprentices develop structured analytical thinking that supports evidence鈥慴ased decision鈥憁aking.
They learn how to:
- Model uncertainty and variability within data
- Test assumptions and evaluate evidence systematically
- Identify and interpret relationships and patterns
- Recognise limitations and risks within analysis
By applying analytical techniques to realistic datasets and workplace鈥憆elated problems, apprentices gain experience in interpreting results and communicating findings clearly. This prepares them to engage confidently with both technical and non鈥憈echnical stakeholders and to contribute effectively to:
- Reports and analytical summaries
- Dashboards and performance metrics
- Business cases and insight鈥憀ed initiatives
Applying software and analytics in organisational contexts
Year one also emphasises professional software development practices that underpin effective analytics.
Apprentices learn how analytical systems are designed and applied in practice, including how to:
- Access and query data using database technologies
- Structure analytics pipelines to ensure accuracy and reliability
- Apply reproducible and well鈥慸ocumented development practices
- Consider ethical responsibilities and responsible data use
By the end of the year, apprentices can design, implement and apply analytical solutions that are technically sound, well documented and aligned with organisational needs. They are well equipped to contribute to data projects, support continuous improvement and add value through thoughtful, methodical use of data.
Year one modules
- Foundations in Software for Data Analytics (20 credits)
- Foundations in Maths for Data Analytics (10 credits)
- Probability and Statistics for Data Analytics (30 credits)
- Applied Software Development for Data Analytics (20 credits)
- Applied Data Modelling and Analytics (30 credits)
Year two
Year two of the programme focuses on applying learning in practice through a substantial, workplace鈥慴ased analytics project. Apprentices consolidate and demonstrate the data analytics skills developed in year one, applying them to real organisational challenges and preparing for end-point assessment (EPA).
At this stage, apprentices follow one of two possible professional practice routes. These routes reflect differing levels of technical depth and are designed to align with both organisational needs and individual development. Progression to either route is not automatic and is jointly agreed by the employer, the university and the apprentice, informed by performance to date, demonstrated capability and future ambition.
The Professional Practice in Data Analytics route focuses on planning, delivering and communicating a complete analytics project using appropriate tools and techniques. This route supports apprentices who are developing strong, applied analytics capability at Level 4, or where analytics plays a supporting role within their wider professional responsibilities.
The Advanced Professional Practice in Data Analytics route provides extended challenge for apprentices who have demonstrated readiness to work with more quantitative and mathematically driven analytical approaches. This route allows for deeper engagement with modelling and analytical reasoning where this is appropriate to the role and organisational context. Availability of this route is subject to agreement and is not guaranteed.
Both routes place a strong emphasis on professional judgement, ethical practice and clear communication. Apprentices are supported to justify their analytical choices, present findings confidently and compile a robust portfolio of evidence aligned to the required Knowledge, Skills and Behaviours. Preparation for the EPA is embedded throughout year two.
By the end of year two, apprentices will have delivered a substantial workplace analytics project, demonstrated their competence through the EPA, and provided clear evidence of their ability to apply data analytics effectively and responsibly within their organisation.
Workplace impact in year two
Across year two, apprentices are supported to:
- Deliver a meaningful analytics project that addresses a real organisational need
- Apply appropriate analytical methods with confidence, rigour and professional judgement
- Communicate insights clearly to stakeholders through structured explanation and evidence
- Demonstrate ethical, reproducible and reflective practice in data analytics work
- Provide employers with clear assurance of applied capability through EPA鈥慳ligned evidence
Year two modules
Professional Practice in Data Analytics (10 credits)
or
Advanced Professional Practice in Data Analytics (10 credits)
Gateway
Once the apprentice has completed all their on-programme learning, a meeting will take place between their employer and the university. During this meeting, the apprentice鈥檚 knowledge, skills and behaviours will be assessed to determine whether they have met the minimum requirements set out in the Data Analyst Apprenticeship standard. Apprentices deemed to have met these requirements will progress onto the end-point assessment (EPA).
End-Point Assessment
The final part of the apprenticeship is the end-point assessment. The end-point assessment requires apprentices to demonstrate that their learning can be applied in the real world. Apprentices will undertake:
- a project with presentation and questioning
- a professional discussion with portfolio