Level 7 AI and Data Science Apprenticeship

Eligibility

Accelerating data science careers

Cambridge Spark’s Level 7 Data Science and AI Apprenticeship is a unique opportunity for professionals and early talent to accelerate their data career, learn cutting-edge skills and earn a MSc-equivalent qualification in data science through their work.

Over 15 months, apprentices will be supported by a data mentor, a personal coach and the Cambridge Spark faculty to learn cutting-edge skills and apply them in the workplace. Learners employed in England may be eligible for full programme funding by the UK Apprenticeship Levy.

Who is this AI and data science apprenticeship is for?

Experienced professionals already using Python to work with complex datasets on a regular basis, who want to apply the latest, cutting-edge data science and AI tools in their work
Early careers candidates looking to advance their career as a data scientist

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.

Learner Outcomes:

Identify and devise data-driven AI solutions to address business opportunities
Automate and optimise business processes with data science
Provide technical authority and insights for the business on data science and AI that are relevant to business goals

Organisation Outcomes:

Build capability to enable and deploy AI across the organisation
Drive business growth with AI solutions to address business opportunities
Reduce costs through automating and optimising manual processes

After my computer science degree dipped into data mining, I had a glimpse of how large-of-an-area data science is. By using the skills you learn at work, you are able to get a much more wholesome picture than you could from a typical Master’s degree - where the biggest limitation is the lack of importance placed on production and cloud engineering.”

Nicholas Orford-Williams, Data Scientist Apprentice at BBC

24/7 immediate feedback

EDUKATE.AI is our learning platform designed for data science education which gives learners immediate and personalised feedback on their code.

Fast skills deployment

Learners apply their skills to real datasets from their first day of learning, with assignments on EDUKATE.AI simulating a working industry environment.

Tailored expert curriculum

A modular curriculum developed with leading experts from academia and industry to meet all skills needs in an organisation.

Peer support

Engagement and support from peers through Knowledge Base, our Q&A feature built into EDUKATE.AI.

Easy set up

Our cloud-based platform requires no installation or set-up for our learners, with their content available whenever they need it.

Reporting

Real time analysis of learner progress and completion at an individual and cohort level allowing us to target support and celebrate success.

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.

The Data Science Toolbox

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

Data Science for Business

Identify practical applications and use cases for Data Science & AI to deliver and create business value.

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 Learning

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

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.

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.

Data Privacy, Ethics and Regulations

Get up to speed with the essentials covering ethical, legal and regulatory issues relating to the use of data.

Practical Hackathon

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

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 Techniques for Model Evaluation

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

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.

Academic Reading Club

These quarterly sessions guide our learners through the latest research within the AI and data science domains.

Software Engineering 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.

Databases SQL & 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.

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 & Gaussian Processes

Look 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 elearning supported by EDUKATE.AI through to tailored bootcamps, to our structured apprenticeship programmes. The Level 4 Data Analyst 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 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 4 Data Analyst 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.

Who's benefitted from our data apprenticeships

Read case studies on the impact we have made for our clients.

Delivering data-driven customer solutions at Royal Mail with apprenticeships

Data Analyst Apprentice identifies £30k of savings for GSK

Building AI capability in media and broadcasting with AI apprenticeships