Course overview
It’s ideal for someone working in an organisation that analyses high-volume or complex data sets using advanced computational methods.
The apprentice discovers and devises new data-driven AI solutions to automate and optimise business processes and to support, augment and enhance human decision-making.
Taught by experts in machine learning and intelligent systems, the course involves practical exercises, simulations, case studies and real-world examples to reinforce learning.
The apprentice applies their skills in supervised and mentored project work, where they are responsible for the entire project lifecycle from conception to completion.
We are a flagship provider of compelling, future-focused professional apprenticeships, enriched by international academic excellence in research and innovation and developed in partnership with industry.
The aims of the course are to:
- develop the apprentice’s knowledge of the AI data specialist discipline
- enable the apprentice to make reasoned, critical decisions for selecting or implementing appropriate AI data analysis solutions to complex real-world problems
- equip the apprentice with technical and interpersonal skills to influence decision-making and strategy within an organisation
- develop the apprentice’s cognitive and critical reflection skills to equip them for a professional career as an AI data specialist
- give the apprentice an appreciation of professional, ethical and legal responsibilities of an AI data specialist.
Please note, we can only respond to enquiries from employers, or individuals with agreement from their employer to undertake an apprenticeship.
Course details
Course structure
Year 1 core modules
The apprentice learns to visualise data and design impactful dashboards, identifying trends and patterns, make business forecasts, informed decisions and present complex datasets in a clear and engaging form.
Machine Learning and Deep Learning
The apprentice develops technique in machine learning and investigates new developments in neural networks and deep learning to be able to make predictions on unseen data.
The apprentice explores quantitative and qualitative techniques for data science, including correlation testing, regression, data categories, normalisation and how to apply them.
Year 2 core modules
The apprentice covers a wide range of AI and data science project development specifics, gaining knowledge and skills necessary to excel in this role.
Modules offered may vary.
How you learn
The course includes work-based elements and blended (on-campus and online) learning with the following delivery patterns:
Course offered at our Middlesbrough campus:
Day release: typically one day a week on-campus.
or
Blended: two days on-campus. Typically one day in September and one day in January. The remaining course is delivered through online classes.
Course offered at our London campus:
Blended: two days on-campus. Typically, one day in September and January. The rest is delivered through online classes.
Lectures introduce and develop material and include research issues and recent developments. The apprentice explores subjects in-depth and develops intellectual skills through tutor-led seminars, practical workshops, individual or group research, and by contributing to discussion forums.
The apprentice must:
- complete training to develop the knowledge, skills and behaviours outlined in this apprenticeship’s occupational standard
- compile a portfolio of evidence.
How you are assessed
Assessments are based on work-related assignments with case studies, technical exercises, reports and some exams.
An end-point assessment (EPA) looks at the knowledge, skills and behaviours developed to determine if the requirements of the occupational standard have been met.
For the EPA, apprentices complete a project report, a presentation followed by questioning, a professional discussion using question bank and a technical test.
See Artificial Intelligence (AI) Data Specialist/Institute for Apprenticeships and Technical Education for further details.
Entry requirements
To be accepted on to an apprenticeship course you must have support from your employer and meet the course entry requirements.
In addition to the standard entry requirements, individual employers will set the selection criteria, whichis likely to include a related degree, or a non-related degree with at least one year’s experience in a technical role and appropriate mathematical skills.
Before starting their Teesside University apprenticeship, learners must hold Level 2 qualifications in English and maths.
Find out more.
Some employers will accept relevant experience and may use entry tests to check abilities.
For general information please see our overview of entry requirements
Employability
Career opportunities
Typical job titles include:
- AI strategy manager
- AI engineer
- AI specialist
- Machine learning engineer
- Machine learning specialist
