Data Scientist
Degree Apprenticeship
PROGRAMME HANDBOOK
Contents
Northeastern University London Centre for Apprenticeships
02 Contents 03 Northeastern University London Centre for Apprenticeships 04 Apprentice Experience & 04 Programme Overview 06 Programme Structure and Courses
Apprentice 12 Support & Guidance
13 Line Manager Responsibilities
Initial Eligibility 15 Review Qualifying for 16 Apprenticeship Funding
08 Welcome Week and Induction
17 H ow Employers Funding Options Can Fund for Employers Apprenticeships
How Apprentices 11 Learning Learn: On the Structure: On Job Learning the Job Learning
Through the integration of academic study and practical workplace experience, apprentices apply their learning in realworld contexts while supporting individual career development and delivering value to employers.
IT & Software 14 Requirements
Assessment 07 Methods & EndPoint Assessment
09 Mandatory Bootcamps
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11 H ow Apprentices Learning Learn: Off the Structure: Off Job Learning the Job Learning
Northeastern University London is a leading higher education institution with a strong focus on academic excellence and applied learning. The Centre for Apprenticeships delivers programmes that combine rigorous academic study with structured workplace learning, enabling apprentices to develop the Knowledge, Skills and Behaviours needed to excel in their roles and contribute to organisational success.
Application and 18 Onboarding Process
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Apprentice experience Apprentices benefit from a structured learning experience that supports their successful integration into the workplace and the wider Northeastern University London community, fostering professional development, meaningful connections, and long-term success.
Programme overview
• Participation in bootcamps and programme events
The Data Science Degree Apprenticeship combines workbased learning with online study over 36 months plus the End-Point Assessment (EPA). Each academic year spans approximately 46 weeks and is designed to integrate academic study with practical workplace application.
• Dedicated academic support to help apprentices reach their full potential
Successful completion of the programme leads to a BSc (Hons) in Data Science.
Support and opportunities include: • Employer engagement and collaboration • Career development activities • Industry talks and guest speaker sessions
• Flexible online learning with access to course materials and recorded lectures • A world-class online library • Collaborative tools to support professional commitments
This apprenticeship is aligned to the Level 6 Data Scientist Apprenticeship Standard (ST0585): Discover the KSBs here
Teaching and learning delivery model The programme is delivered through a flexible, structured model designed to combine academic rigour with practical workplace application. Courses are organised into six- or twelveweek study blocks, blending asynchronous study with scheduled live teaching sessions to provide both flexibility and structure.
Learners are supported through a range of teaching methods and resources, including: • Guided online learning with rich media assets and interactive activities designed to deepen understanding and application • Live collaborative seminars for each module, delivered by expert faculty, with recordings available for flexible access and review
• Online discussion groups to encourage collaboration, knowledge exchange, and peer learning • Dedicated 1:1 academic support with experienced, specialist faculty • Study skills workshops to support English, Maths, and academic practice, ensuring every apprentice is equipped to succeed Each module includes multiple assessment points to measure progress, reinforce learning, and support the development of core Knowledge, Skills and Behaviours.
• Access to a world-class digital library, including an extensive collection of e-books, peer-reviewed journals, databases and academic research materials
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Programme structure and courses
Assessment methods
The apprenticeship is delivered over three years, integrating academic study with practical workplace experience. Each year builds progressively, developing the Knowledge, Skills and Behaviours required for a career in Data Science.
The programme employs a range of assessment methods designed to evaluate both academic knowledge and professional practice. Each method is aligned to the competencies required of a qualified Data Science professional.
YEAR 1
YEAR 2
• Business Fundamentals (15 Credits)
• Information Technology Project Management (15 Credits)
• Intensive Foundations of Computer Science and Programming (30 Credits)
YEAR 3 • Software Engineering (15 Credits)
• Database Design and Management (15 Credits)
• Advances in Data Science and Data Management (30 Credits)
• Mathematical Structures
• Algebra and Probability for
• Implementing Data Science
and Methods (15 Credits)
Data Science (15 Credits)
• Databases and Data Management (30 Credits)
• Machine Learning and Data Mining (30 Credits)
• Cloud Computing (15 Credits)
• Data Synthesis (30 Credits)
• Data Science Bootcamp (15 Credits)
• Data Visualisation (15 Credits)
(15 Credits) • Data Science Synoptic Project and End-Point Assessment (60 Credits)
• Data Analytics (15 Credits)
Further details are available on the Level 6 Data Science Apprenticeship webpage
• Written Assignments – Essays and reports in which learners identify a workplace problem and propose a solution, directly linked to course content and teaching material. • Portfolios — An extended report based on a work-based project, completed as part of the Data Science Synoptic Project and EPA AE2. • Examinations — MCQ and computer-based exams testing theoretical knowledge. • Set Exercises — Mathematical exercises assessing the application of quantitative and analytical techniques.
Presentations — A video recording of the learner presenting a proposed solution, assessed in Implementing Data Science Summative 2. Practical Assessments — Coding assignments evaluating learners’ technical ability to write and apply code. Role Plays — Group presentations assessed through Data Synthesis Summative 2. Artefacts — A poster produced for Data Synthesis Summative 1, demonstrating the ability to present data insights visually. In-Person Multiple Choice Examination (Year 3 only) — Taken in person in the final year, this exam tests comprehensive knowledge accumulated across the programme as part of the Data Science Synoptic Project. For Higher and Degree Apprenticeship programmes, all assessment elements are compulsory pass elements. Non-submission of assessment elements results in automatic failure of the course.
F or full details of assessment methods, refer to the Course Descriptors.
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End-Point Assessment The programme enables apprentices to combine onthe-job project work with independent research and supervisory meetings, totalling approximately 600 hours of learning. Apprentices will apply advanced knowledge and skills to real-world challenges, such as developing data-driven solutions, building predictive models, analysing complex datasets, or introducing datainformed improvements within their organisation. Assessment is carried out through an examination (20%, 1 hour), a written assignment (50%, 7,500 words), and a presentation (30%, 90 minutes), supported by a professional discussion and a portfolio of evidence. Throughout their studies, apprentices will develop expertise in data science theory, methods, tools, and stakeholder engagement, while strengthening professional skills such as critical analysis, technical communication, and evidencebased decision-making. For full details of the EPA, refer to the official End-Point Assessment Overview. Further details on the End-Point Assessment are available in the Data Science Synoptic Project and End-Point Assessment Course Descriptor. 7
In Person Bootcamp Mandatory bootcamps
Year 1 and Year 2 include a mandatory two-week Bootcamp, that forms a core part of the apprenticeship programme. The bootcamps are directly tied to key programme modules, with each year focusing on distinct areas of study:
Year 1
Data Science Bootcamp
Year 2
Data Synthesis
Structure Week 1 – Mandatory in-person attendance at the London campus: Face-to-face teaching aligned with core modules Week 2 – Online: Daily registration with the
Welcome week and induction
Attendance is compulsory, and employers must release apprentices for the full duration.
course leader, followed by independent study, group work, and assessments, ending in a Friday group presentation to assessors.
Apprentices begin the programme with a Welcome Week in Year 1. At the start of Years 2 and 3, a full Induction Week is also completed. Attendance is mandatory across all three years, and employers are required to release apprentices for the full duration of each induction week.
Employer requirements
Important information
Employers must release apprentices for the full duration. Attendance is mandatory across both weeks, Monday to Friday.
Bootcamp activity contributes towards off-thejob learning requirements.
The Welcome Week introduces the programme, key systems, expectations, and available support services.
Induction activities include:
The Induction Weeks in Years 2 and 3 provide programme updates, refresher guidance, and preparation for the academic year ahead.
• Guided independent (asynchronous) learning activities
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• Live, tutor-led (synchronous) sessions
These activities ensure apprentices are fully prepared with the knowledge, systems, and support required for success in academic study and workplace learning.
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How apprentices Learning learn Structure Off-the-Job Learning
Apprentices are required to complete a minimum of 7 hours per week of off-the-job learning in line with funding requirements. This may be completed as a full day or split across the week, depending on learner and employer needs. Employers must ensure apprentices are released to complete this structured learning, which supports academic progress and workplace application.
On-the-Job Learning
On-the-job learning enables apprentices to apply knowledge and skills in real workplace situations, progressively taking on greater responsibility under supervision. Employers must provide meaningful work aligned to the apprenticeship standard, along with regular feedback and support. This ensures apprentices develop the required Knowledge, Skills and Behaviours (KSBs) and are fully prepared for apprenticeship assessment.
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Line manager responsibilities Line managers play a key role in apprentice success by ensuring workplace learning is aligned with programme requirements. Responsibilities include: • Releasing apprentices for off-the-job
learning, induction, and bootcamps • Supporting attendance at scheduled
teaching sessions • Participating in 8-weekly tripartite reviews • Providing meaningful work aligned to
Knowledge, Skills and Behaviours (KSBs) • Offering regular feedback and
Supporting your success
performance guidance • Supporting preparation for the
apprenticeship assessment
Apprentices benefit from a structured programme of academic provision, pastoral care, and professional development opportunities designed to support success in both study and workplace learning. Academic Progress and Programme Oversight
Wellbeing and Personal Development Services
• Dedicated Success Manager
• Mental health and counselling provision
• 8-weekly tripartite reviews involving apprentice, employer, and training provider
• Student services and wellbeing support specialists
• Monthly progress reviews
• Peer mentoring and community networks
• Structured programme monitoring and feedback cycles
Learning, Teaching and Academic Resources
Community, Engagement and Enrichment • Student Union and learner voice opportunities
Apprentices have access to a range of learning and teaching resources, including:
• Employer engagement activities and collaborative events
• 1:1 academic guidance with faculty
• Induction programme, bootcamps, and graduation events
• Study skills workshops and guided learning sessions
• Awards and recognition opportunities
• Online learning platform and digital library, including academic journals and research materials • Recorded lectures and digital learning materials to enable flexible study 12
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IT & software requirements Apprentices must have access to a laptop or desktop computer throughout the programme; a laptop is strongly recommended for in-person sessions. Employers are responsible for ensuring this is in place before the programme starts, including any administrative access needed to install software. University Email All apprentices are assigned a university email address. This must be checked regularly as all correspondence is sent there, not to their work email.
University Platforms Apprentices will access the following using their university credentials: • Office 365 — Word, Excel, PowerPoint and Teams • Canvas — learning materials, assignment submissions and study resources • Maytas — records apprenticeship activity and offthe-job hours • Quercus — course details and assignment marks • Northeastern University Library — e-books, e-journals and academic resources • Zoom — synchronous teaching and seminars
Essential Software Employers must ensure that each learner has access to the following software for their academic studies. Software may require administration rights to install. Year 1 • Python — main programming language used throughout the degree; install the latest stable version from python.org (ensure both check-boxes, including the path option, are checked during install so pip works correctly) • Additional Python Libraries — matplotlib, seaborn, Pandas, Networkx, Sqlite3
• Jupyter Notebook — interactive Python environment for annotated code and data analysis • Python IDE — e.g. IDLE, Visual Studio Code, or PyCharm • SQLite3 — compact, selfsupporting relational database management system • Putty — SSH client used to connect to Linux virtual machine instances • Miro — online whiteboard and collaborative working platform • Tableau — data visualisation tool used across the degree (free 1-year student account via university email) • VSCode — code editor • Git — version control software • Docker — container management solution Students must also be able to create and use Python virtual environments (venv) and install packages via pip on their own laptop, on Windows, macOS, or Linux. Learners should also have access to the following web applications: Deepnote, MermaidCharts, Trinket, diagrams.net, Figma, Miro, and GitHub. Year 2 In addition to the Year 1 software list, the following will be required: • MySQL and MySQL Workbench — relational database management system, with Workbench providing a GUI for database design (e.g. EntityRelationship diagrams)
• MongoDB and MongoDB Compass — NoSQL database, with Compass providing a GUI for managing the database (browser-based, no installation required) • Google Cloud Platform — access required; no installation necessary • Additional Python Libraries — sklearn, Altair, stattools, cartopy, pytest, mysqlconnector-python, Plotly, Numpy, OpenCV, NLTK, PIL, spaCy • PyTorch — torch, torchvision and torchaudio — used for computer vision and natural language processing • TensorFlow — used to build and run Machine Learning models, optimised for large datasets and GPU/CPU use Year 3 In addition to the Year 1 and Year 2 software lists, the following will be required: • PythonMeta — used for Evidence-Based Medicine (EBM) tasks • Node JS — JavaScript runtime built on Chrome’s V8 engine • NPM — Node Package Manager
Eligibility and funding Initial Eligibility Review
Before progressing an application, an initial eligibility review is carried out to ensure the role and candidate are suitable for the apprenticeship standard As part of the initial eligibility review, employers and prospective apprentices are required to submit a current CV and job description for the role. Northeastern University London will review these against the apprenticeship standard to confirm that the role aligns to the standard, the apprentice is undertaking relevant duties, and the candidate is likely to meet the entry requirements. Where alignment is not immediately confirmed, feedback and guidance will be provided before progressing to formal application and onboarding.
• MATLAB — matrix manipulation, plotting, algorithm implementation, and user interface creation • Additional Python Libraries — yfinance, html5lib, Beautifulsoup4, lxml Software requirements may be updated annually to reflect current industry practice.
Entry Requirements Apprentices must be employed in a role aligned to the Data Scientist apprenticeship standard. Entry requirements are typically: • Three A-levels (or equivalent) at BBB or above • GCSE Maths and English at grade 4/C or above Relevant prior learning and workplace experience will also be considered, and Recognition of Prior Learning (RPL) and credit transfer may apply where appropriate. Where an applicant does not fully meet the entry requirements, each case will be reviewed individually. In some instances, applicants may be invited to interview and/or submit a portfolio before a final decision is made.
Don’t meet funding criteria? You can still join our programme via our commercially-funded route – ask our BD team for more information. 15
Qualifying for apprenticeship funding
Funding Options for Employers
To be eligible for apprenticeship funding, applicants must meet government funding rules.
A breakdown of the funding options available, depending on your organisation’s size and levy status.
Applicants must:
Levy-Paying Employers
Non-Levy Employers
Key Consideration
Employers with an annual payroll over £3 million contribute to the Growth and Skills Levy (formerly the Apprenticeship Levy) and can use these funds to pay for apprenticeship training and assessment. Funds are accessed and managed through the Apprenticeship Service account.
Non-levy employers: funding depends on apprentice age at the start of training
Levy transfer arrangements can take time to secure. Employers should begin the process at least 2–3 months before the intended apprenticeship start date.
• Meet UK residency and funding eligibility requirements in line with government funding rules • Be at least 16 years old at the start of the programme • Have left full-time education prior to starting the apprenticeship • Be employed for a minimum of 30 hours per week • Be able to complete the apprenticeship within any relevant employment or visa conditions • Not hold an existing qualification at the same or higher level in a related subject area
Where applicants do not hold GCSE Maths and English at grade 4/C or above, they will be required to work towards these qualifications as part of the apprenticeship, in line with funding requirements. If there is uncertainty about an applicant’s eligibility, please contact the Business Development Team before progressing an application. A link to the official apprenticeship funding rules can be found here: Apprenticeship Funding Rules
Discover how to make the most of your levy with our Levy Health Check. If levy funds run out, training is fully funded for apprentices aged 16 to 24. For those aged 25 or over, employer co-investment rises to 25% (previously 5%).
Under 25 • From Sept 2026 starters: no training costs (100% funded) 25 or Over: Co-Investment • Employer pays 5%, government funds the remaining 95% Levy Transfer • Employers may request unspent levy funds from larger organisations
Funding arrangements must be confirmed before the apprenticeship begins. Employers are responsible for ensuring an appropriate funding route is in place prior to enrolment. Learn more about funding options available to SME organisations in our
• These funds can be used to cover training and assessment costs
Apprenticeship Funding Guide for SMEs.
• Employers can explore available pledges or requesting via the official government portal
Additional Support
• Successful applicants have 100% of costs covered
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Funding Requirement
• NI Exemption (under 25) • Care Leaver Bursary: £3k • Hiring Incentives: £1k-£3k • Youth Jobs Grant: £3k
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Application process The steps below outline what to expect.
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Submit an application form
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Complete a Skills Scan selfassessment
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Complete initial English and Maths assessments
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Attend Skills Scan discussion and ID check with the employer and Northeastern University London
What happens next Our operations team reviews your full application and confirms the outcome. Some applicants who do not meet the academic entry criteria may be invited to interview as part of this process.
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Contacts and support All enquiries relating to the Data Scientist Degree Apprenticeship should be directed to the Business Development Team. The team can support with: • Programme information and guidance • Employer queries • Eligibility and entry requirements • Application, onboarding, and enrolment process • Funding and next steps Email: apprenticeships@nulondon.ac.uk If your query requires specialist input, the Business Development Team will ensure it is directed to the appropriate contact at Northeastern University London.
Devon House 58 St Katharine’s Way London E1W 1LP United Kingdom