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AI and Data Science Apprenticeship (Level 7)

Build and productionise predictive models by leveraging machine learning and data science tools and techniques.

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One data science learner can create real business impact

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£2m profit increase from data-driven insights

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£100,000 saved by reducing reliance on consultancies

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208 hours saved by automating analysis tasks

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5 x faster ML model training achieved through automations

Deliver on your analytics and AI strategy

Over the duration of the programme, Cambridge Spark's Level 7 AI and Data Science Apprenticeship equips employees with the skills to deliver transformational data science projects that drive competitive advantage.

The technical curriculum covers critical tools and techniques required including deep learning, supervised and unsupervised learning and product management for AI.

Eligibility

Suitability of role

  • 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 can identify use cases within their work for predictive models

Eligibility for funding

  • No prior Data Science degree or related experience.
  • Employed in England and resident in the UK or EEA for the last 3 years
  • Employees working at least 30 hours a week (part-time employees can be considered for a minimum cohort size)
  • Can commit to the minimum 6 hours a week off the job learning requirement for the duration of the programme (15 months of training)

Who is this AI and data science apprenticeship is for?

Professionals looking to create business impact by deploying data science tools and building machine learning models.

No strong experience in Python and maths?

Our Level 4 Data Analyst Apprenticeship is the perfect starting point to learn Python and build a career in data science and AI.

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.

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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.

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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 first to graduate learners. We continue to have the largest number of AI apprentices on programme, with 79% of AI apprentices choosing our programme.

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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.

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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.

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Expert Curriculum with Specialist Electives

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

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Personalized 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 personalized support structure helps learners to succeed and overcome obstacles they encounter.

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Flexible Fully Online Learning

Our program 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.

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Vibrant Community

Joining our program 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.

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A real-world learning experience

EDUKATE.AI is our learning experience platform which delivers a seamless experience in one place and accelerates learning and impact through real practice on real projects with immediate, personalised feedback on code.

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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.
We take a modular approach to how we offer our curriculum. Our core curriculum provides learners 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.

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.

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 modeling, Monte Carlo methods and the fundamental concepts behind Bayesian machine learning.
 

Enquire now

Fill out the following form and we’ll contact you within one business day to discuss and answer any questions you have about the programme. We look forward to speaking with you.

FAQs

What delivery options do you offer?

We tailor our delivery to your workforce needs. This ranges from from independent, immersive elearning supported by EDUKATE.AI through to tailored bootcamps, to our structured apprenticeship programmes. The Level 7 AI and Data Science Apprenticeship is available to learners based in England.

Are you able to tailor the programme to the organisation and sector?

Yes. We work with our clients to contextualise our programmes to their organisation and sectors they operate in. We do this through tailored hackathons, bespoke assignments and guest lectures from industry experts. We also work with a range of partners to create bespoke programmes for sector, such as health and journalism.

What is an apprenticeship?

Apprenticeships are a long-term training commitment which seek to support people entering the workforce and upskill existing UK-based employees within an organisation, enabling employers to foster a workforce consisting of highly-skilled and highly-engaged talent.

The Cambridge Spark AI and Data Science Apprenticeship runs 15 months plus a 3-month end-point assessment and includes a minimum of 6 hours per week off-the-job training, enabling a blended approach between theory and practical-learning.

What is the Apprenticeship Levy?

The UK government introduced the Apprenticeship Levy scheme in April 2017 as a way to drive investment in strengthening the country’s skills base.

All organisations with annual staff costs of over £3m have to pay 0.5% of their salary bill into a ring-fenced apprenticeship levy pot. The money is collected monthly via PAYE and can only be used for training on approved apprenticeship schemes (such as the Level 7 AI and Data Science Apprenticeship that we offer). Organisations must forfeit any levy funding left unspent for 24 months or more.

What if my organisation doesn't pay into the UK Apprenticeship Levy?

An organisation that doesn't pay into the levy can still qualify for government-funded apprenticeships for their staff. In fact, the UK government will sponsor 95% of the apprenticeship programme, leaving the organisation to invest the remaining 5%, provided that learners meet other eligibility criteria.

What does "off-the-job training" mean?

Off-the-job training is defined as learning undertaken outside of the day-to-day work duties and during the apprentice’s normal working hours.

Our off-the-job training is delivered on a flexible basis and can be carried out at the apprentice’s place of work or home.
The 6 hours per week, minimum, off-the-job training provides learners with the time to focus and develop the required skills, knowledge and behaviours to complete the programme.

How much do managers need to be involved?

Managers will need to ensure apprentices achieve their planned off-the-job training hours and work on their project portfolio.

We also encourage managers to have regular one-to-one meetings with apprentices to catch up on how they are progressing and to join the apprentice and their coach for 30 minutes every 3-4 months for a general catch up about the programme.

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