Level 7 AI and Data Science Apprenticeship

One Data Science Learner Can Create Real Business Impact

£2m profit increase from data-driven insights

£100,000 saved by reducing reliance on consultancies

208 hours saved by automating analysis tasks

5 x faster ML model training achieved through automations

Deliver on Your Analytics & AI Strategy

This programme is designed for employers looking to develop early-career AI and data science talent through a fully funded Level 7 apprenticeship.

Duration: 15 months, plus 3 months assessment

Price: Fully funded by the Apprenticeship Levy for learners aged 16-21 at the start of the programme.

Cohort Type: Closed cohorts only

Deliver Measurable Business Value

Apprentices deliver real outcomes through applied AI and data projects.

Build In-House AI & Data Capability

Develop strategic AI skills internally and reduce reliance on external consultants.

Real-World Application

Hands-on learning using real data, tools and workplace challenges.

Expert-Led Delivery With Dedicated Support

Specialist trainers and coaches support learners throughout the programme.

Enquire about a closed cohort

What Makes Our Programme Special

We deliver all of our programmes online, helping our clients offer flexible and inclusive programmes open to all of their staff. EDUKATE.AI, our online learning platform, gives learners a sandbox environment to practice their skills, providing them with immediate feedback on industry-simulated assignments. We believe that the gold standard for online delivery is to offer a mix of experiential learning, coaching, technical mentorship and peer support.

Winner of Best Course in AI

This course won the CogX Award in 2023 for "Outstanding Achievements & Research Contributions: Best Course in AI". This award highlights the best tutorial or course that targets the rapidly growing need for technical expertise in AI.

Pioneers in AI Apprenticeships

We helped to create the first AI apprenticeship in the UK and were the first provider to offer the programme and the first to graduate learners.

Instant Feedback for Accelerated Learning

EDUKATE.AI provides instant feedback on assignments, with our learners having benefited so far from over 550,000 pieces of immediate feedback. This unique feature accelerates learning outcomes by allowing learners to see where they need to improve and make corrections in real time.

Real-World Practice for Accelerated Impact

EDUKATE.AI provides a sandbox environment where learners can practice new skills on real datasets. This accelerates the impact that learners can make in their workplace, allowing them to immediately apply what they've learned.

Expert Curriculum with Specialist Electives

Each module of our programme is delivered by specialists in that field, ensuring that learners receive comprehensive and up-to-date knowledge. The L7 offers three cutting-edge elective pathways, allowing learners to specialise in areas that are most relevant to their goals.

Personalised Learner Support

We provide each learner with a dedicated Data Mentor and Learner Success Coach to support them on their technical and personal development. This personalised support structure helps learners to succeed and overcome obstacles they encounter.

Flexible Fully Online Learning

The programme is fully online, providing maximum flexibility for learners and employers alike. This means that learners can access their content from anywhere, with no set up or installation of EDUKATE.AI required.

Vibrant Community

Joining our programme means becoming part of a thriving community of thousands of data professionals. Learners have the opportunity to tap into this rich network of peers and alumni and benefit from the expertise and experience of others in the field.

Eligibility, Funding & Commitment

The L7 AI & Data Science Apprenticeship is for professionals looking to create business impact by deploying data science tools and building machine learning models.

Suitability of Role

Early-career AI and data science talent
Regularly using Python in their work or can have the opportunity to
Proficient with both statistics and linear algebra
Working in a role where they are regularly using and analysing data
Looking to build predictive models and identify use cases within their work for predictive models
This Apprenticeship uses the L7 Artificial intelligence (AI) data specialist standard

Eligibility for Funding

Meet the age criteria on entry (16-21 at start of programme)
No prior Data Science degree or related experience.
Employed in England and resident in the UK or EEA for the last 3 years
Option to opt in / out of Functional Skills (for 19+ years of age), depending on prior attainment of Level 2 / GCSE English and Maths (or equivalent) at grade C or above.
Can commit to the minimum planned weekly hours off the job learning - required for the duration of the programme.

Enquire about a closed cohort

The Curriculum

We deliver a cutting-edge curriculum developed by our leading faculty, made up of data scientists in leading industry positions and academics from some of the top universities in the world. Our curriculum is continuously updated and reiterated to incorporate the most recent techniques coming out of industry and academia.

Our core curriculum provides learners with everything they need to thrive in a data science role and meet all requirements for the Level 7 Data Science Apprenticeship. Our elective specialist pathways offer learners the ability to stretch themselves and specialise in new technical areas.

Core Modules

The Data Science Toolbox

Use of some of the most common, industry-standard tools for conducting data analysis and data science in Python.

Introduction to Machine Learning

Build familiarity with a range of advanced concepts and tools required to use different types of machine learning models and techniques.

Product Management for AI

Develop a customer-centric product mindset and focus on understanding users to build products that solve their problems and serve their needs.

Supervised Classification

Use an array of discriminative and generative supervised learning models as well as sophisticated techniques to evaluate model suitability and improve model performance.

Ensemble Methods

Gain familiarity with ensembles, covering a range of key concepts including bagging and random forest, boosting & gradient boosting, stacking, advanced SKlearn Techniques & Support Vector Machines.

Pragmatic Model Evaluation

Gain familiarity with a suite of evaluative techniques to tackle different types of data science problems for different situations and purposes.

Unsupervised Learning

Learn a range of unsupervised learning models and techniques to reveal latent structure within data, including KMeans, hierarchical clustering, DBSCAN, PCA and t-SNE.

The AI Landscape

Explore the ethical considerations surrounding AI, as well as an examination of data privacy regulations and their impact on AI development and deployment.

Time Series Analysis

Build an advanced understanding of tools and testing techniques for working with time series data with Python, Pandas, Numpy, the Prophet library as well as autoregressive models.

Practical Hackathon

Work collaboratively in teams on a real-world project to apply newly acquired skills in a realistic simulated environment.

Neural Networks and Deep Learning

Learn how neural networks are constructed and trained and how to use them in practice, including CNN, RNN, GANs and Graph Neural Networks.

Model Explainability and Interpretability

Understand the different approaches and techniques for interpreting and explaining a range of machine learning models and deep neural networks.

MLOps Elective Specialist Pathway

Software Data Practices for Data Scientists

Learn about design patterns and software development principles to develop code that is robust and flexible for requirements change.

Software Testing for Data Science

Learn how to test processing functions with unittest, pytest and hypothesis.

Machine Learning in Production

Gain experience in advanced testing, Scikitlearn best practices and how to carry out continuous integration, continuous deployment and monitoring models in production.

Advanced Data Science Elective Specialist Pathway

Natural Language Processing

Learn about the main applications and techniques of NLP and how to build models for and evaluate approaches to supervised and unsupervised sentiment analysis.

Recommender Systems

Understand the practical applications of different types of recommender systems and learn to use the tools to be able to use them in practice.

Bayesian ML and Gaussian Processes

Look at different probability distributions, probabilistic modelling, Monte Carlo methods and the fundamental concepts behind Bayesian machine learning.

DataOps Elective Specialist Pathway

Databases SQL and NoSQL

Learn how to use SQL and NoSQL to store, query and retrieve structured and unstructured data.

Big Data Systems

Learn how to apply and leverage the power of distributed computing to extract value & insight at scale.

Principles of Cloud Computing

Build familiarity with cloud computing infrastructure covering common cloud services, the differences between virtualisation and containerisation and the fundamentals of working with Docker.

For Individuals

Own the AI Workflow

For Organisations

AI for Leaders

About

Revolutionise Data & AI Upskilling with EDUKATE.AI

Resources

The Data & AI Mastery Podcast

AI and Data Science Apprenticeship (Level 7)