Level 7 AI and Data Science Apprenticeship

Delivery

A unique support system to guarantee learner success

We deliver all of our programmes online, helping our clients offer flexible and inclusive programmes open to all of their staff. We believe that the gold standard for online delivery is to offer a mix of experiential learning, coaching, technical mentorship and peer support.

• EDUKATE.AI

  EDUKATE.AI is our learning platform designed for data science education. EDUKATE.AI provides real-world simulated assignments and gives learners immediate and personalised feedback on their code.

• Data mentor

  An experienced data mentor helps learners scope and deliver work-based projects, helping bridge the gap between learning and application to generate a return on investment.

• Professional coach

  A coach supports the career and personal development of our learners through 1:1 coaching sessions, as well as quarterly tripartite reviews with line managers. 

• Expert faculty

  Our world-class faculty of experienced data professionals and academics from leading universities support our learners through workshops and 1:1 tutoring on queries.

• Live expert-led lectures

  Our learners join online workshops delivered by our expert faculty, with the support of a tutor for 1:1 questions in real-time. Learners can access recordings through EDUKATE.AI.

• Peer and expert community

  Learners benefit from a peer network that provides support and technical help. Learners also benefit from our network of experts, with monthly guest lectures from different industries. 

The Curriculum

• The Data Science ToolboxUse of some of the most common, industry standard tools for conducting data analysis and data science in Python.
• Data Science for BusinessIdentify practical applications and use cases for Data Science & AI to deliver and create business value.
• Maths for Data ScienceBuild an advanced understanding of key mathematical concepts that underpin much of the AI and data science domain.
• Introduction to Machine LearningBuild familiarity with a range of advanced concepts and tools required to use different types of machine learning models and techniques.
• Product Management for AIDevelop a customer-centric product mindset and focus on understanding users to build products that solve their problems and serve their needs.
• Supervised LearningUse an array of discriminative and generative supervised learning models as well as sophisticated techniques to evaluate model suitability and improve model performance.
• Unsupervised LearningLearn a range of unsupervised learning models and techniques to reveal latent structure within data, including KMeans, hierarchical clustering, DBSCAN, PCA and t-SNE.
• Time Series AnalysisBuild 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.
• Data Privacy, Ethics and RegulationsGet up to speed with the essentials covering ethical, legal and regulatory issues relating to the use of data.
• Practical HackathonWork collaboratively in teams on a real-world project to apply newly acquired skills in a realistic simulated environment.
• Ensemble MethodsGain 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 Techniques for Model EvaluationGain familiarity with a suite of evaluative techniques to tackle different types of data science problems for different situations and purposes.
• Neural Networks and Deep LearningLearn 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 InterpretabilityUnderstand the different approaches and techniques for interpreting and explaining a range of machine learning models and deep neural networks.
  
• Academic Reading ClubThese quarterly sessions guide our learners through the latest research within the AI and data science domains.
  

MLOps Elective Specialist Pathway

• Software Engineering Practices for Data ScientistsLearn about design patterns and software development principles
  to develop code that is robust and flexible for requirements change.
• Software Testing for Data ScienceLearn how to test processing functions with unittest, pytest and hypothesis.
• Machine Learning in ProductionGain 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 & NoSQLLearn how to use SQL and NoSQL to store, query and retrieve structured and unstructured data.
• Big Data SystemsLearn how to apply and leverage the power of distributed computing to extract value & insight at scale.
• Principles of Cloud ComputingBuild 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 ProcessingLearn about the main applications and techniques of NLP and how to build models for and evaluate approaches to supervised and unsupervised sentiment analysis.
• Recommender SystemsUnderstand 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 & Gaussian ProcessesLook at different probability distributions, probabilistic modeling, Monte Carlo methods and the fundamental concepts behind Bayesian machine learning.

FAQs

What Delivery Options Do You Offer?

We tailor our delivery to your workforce needs. This ranges from from independent, immersive e-learning supported by EDUKATE.AI through to tailored bootcamps to our structured Fellowship and Apprenticeship programmes. The Level 7 AI Data Specialist 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 sectors including health and journalism.

What Is An Apprenticeship?

Apprenticeships are a long-term training commitment which seek to support people into 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 Level 7 AI Data Specialist Apprenticeship is delivered over 15 months plus 3 months End Point Assessment and includes a minimum of 20% off-the-job training, enabling a blended approach between theory and practical-learning.

What Is The Apprenticeship Levy?

The UK government’s Apprenticeship Levy scheme came into effect 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 must be spent within 24 months and used for training on approved apprenticeship schemes (such as the Level 7 AI Data Specialist Apprenticeship that we offer).

What Does 20% Off-The-Job Training Mean?

Off-the-job training is defined as learning which is undertaken outside of the day-to-day work duties and it must take place within 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 from home.

The 20% off-the-job training provides learners with the time to focus and develop the required skills, knowledge and behaviours to achieve the apprenticeship.

How Much Do Managers Need To Be Involved?

Managers will need to ensure apprentices achieve the 20% 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 thirty minutes every 3-4 months for a general catch up about the programme.