One data science learner can create real business impact:
240% more project revenue
achieved through data-driven insights
84% lower project costs
achieved through efficiencies
90% shorter project time
achieved through automations
5x faster ML model training
achieved through automations
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?
- 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
- 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
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.
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.
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.
Real time analysis of learner progress and completion at an individual and cohort level allowing us to target support and celebrate success.
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.
Use of some of the most common, industry standard tools for conducting data analysis and data science in Python.
Identify practical applications and use cases for Data Science & AI to deliver and create business value.
Build familiarity with a range of advanced concepts and tools required to use different types of machine learning models and techniques.
Develop a customer-centric product mindset and focus on understanding users to build products that solve their problems and serve their needs.
Use an array of discriminative and generative supervised learning models as well as sophisticated techniques to evaluate model suitability and improve model performance.
Learn a range of unsupervised learning models and techniques to reveal latent structure within data, including KMeans, hierarchical clustering, DBSCAN, PCA and t-SNE.
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.
Get up to speed with the essentials covering ethical, legal and regulatory issues relating to the use of data.
Work collaboratively in teams on a real-world project to apply newly acquired skills in a realistic simulated environment.
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.
Gain familiarity with a suite of evaluative techniques to tackle different types of data science problems for different situations and purposes.
Learn how neural networks are constructed and trained and how to use them in practice, including CNN, RNN, GANs and Graph Neural Networks.
Understand the different approaches and techniques for interpreting and explaining a range of machine learning models and deep neural networks.
These quarterly sessions guide our learners through the latest research within the AI and data science domains.
MLOps Elective Specialist Pathway
Learn about design patterns and software development principles to develop code that is robust and flexible for requirements change.
Learn how to test processing functions with unittest, pytest and hypothesis.
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
Learn how to use SQL and NoSQL to store, query and retrieve structured and unstructured data.
Learn how to apply and leverage the power of distributed computing to extract value & insight at scale.
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
Learn about the main applications and techniques of NLP and how to build models for and evaluate approaches to supervised and unsupervised sentiment analysis.
Understand the practical applications of different types of recommender systems and learn to use the tools to be able to use them in practice.
Look at different probability distributions, probabilistic modeling, Monte Carlo methods and the fundamental concepts behind Bayesian machine learning.
Hear from Didier Vila, Chief Data and AI Officer at Utility Warehouse
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.