Data Scientist
This course equips learners with practical skills in data analytics, machine learning, and AI. Apprentices gain the expertise to tackle real-world challenges and drive data-informed decision making in organisations.
Level 6 Integrated Degree
BSc in Data Science
32 months + 4 months assessment
Entry Requirements - 96 UCAS points or relevant experience
Fully Funded - £19,000 from the levy
Benefits for Business
- Smarter Decision-Making Equip your staff with advanced data and analytical skills to make informed, evidence-based business decisions.
- Increased Efficiency Leverage AI, analytics and automation to optimise processes, improve productivity and drive performance.
- Future-Ready Expertise Develop in-house talent equipped to tackle evolving challenges, embrace emerging technologies and maintain a competitive edge.
Who is it For?
- Businesses with Data Teams Organisations looking to develop in-house data capabilities and turn data into actionable business insights.
- Aspiring Data Professionals Build a strong foundation in programming, statistics, data analysis and machine learning, with practical experience.
- Career Changers & Professionals Transition into data from business, finance, IT or other fields and unlock new career opportunities.
Course Delivery
In-person blocks (2-3 days) with tutors
Half-day live online sessions with tutors
Self-paced guided study
1-2 hrs of other activity weekly
Approx. 6 hours per week (9:30-17:00)
Developmental coaching
Workplace application through work based projects
Course Curriculum
Year One
Month 1-3
The Digital World
Introductory module exploring the synergies between technology, business, and leadership through interactive sessions on relevant topics.
Month 3-4
Programming Foundations
Programming fundamentals, data handling and analytics, visualisation techniques, control flow and logic, functions and modularity, data structures, algorithms.
Month 4-6
Data Analytics for Business Intelligence
Data Science Life Cycle, covering problem definition, data collection and processing, data analytics, and reporting and visualisation.
Month 6
Professional Practice
Develops career and personal skills, including emotional intelligence, creative thinking, personal branding, corporate social responsibility, and digital literacy.
Month 7-8
Analytical Problem Solving
Essential analytical techniques and tools, including modelling and statistical concepts.
Month 10-12
Strategic Data Analytics
End-to-end analytics process, focusing on developing effective dashboards, analysing key business metrics, and communicating insights to stakeholders in a strategic and impactful way.
Year Two
Month 13-16
Secure Data-Driven Solutions
Experience of each of the stages of the software development lifecycle. A number of cases will be explored to demonstrate a variety of approaches, and highlight the strengths and weaknesses of each approach.
Month 17-20
Machine Learning for Business Impact
Practical experience in data exploration, data management, and machine learning, bridging business requirements with technical solutions.
Month 18
Professional Practice
Develops leadership and professional skills, including negotiation, building high-performance teams, influencing others, and managing risk.
Month 18-21
Big Data and Cloud Computing
Big Data and cloud technologies, focusing on scalable data storage, processing, analytics, and visualisation.
Month 22-24
Business for Digital Professionals
How organisations function, including digital domain and the organisation’s environment. It will also address how digital advances are disrupting business and supporting societal goals.
Year Three
Month 24-27
Artificial Intelligence for Strategic Impact
Practical experience in machine learning and deep learning, developing modelling skills. Learners will explore the role of Data Scientist in the broader organisational environment and the Data Science Lifecycle.
Month 28-32
Professional Project
Plan, research and deliver a complex, workplace-relevant, Data Science project, supervised and mentored by academics to ensure high standards of applied knowledge.
Month 32-36
Apprenticeship Assessment
A knowledge test assessing understanding of data science principles, methods, tools and techniques. Work-based project and report demonstrating the application of data science to a genuine business challenge, including analysis, problem-solving and data-driven recommendations. A professional discussion supported by a portfolio, assessing the apprentice’s ability to demonstrate the required knowledge, skills and behaviours (KSBs).
Designed with industry
Courses built in collaboration with employers.
Applied learning
Real projects, real impact.
Work-integrated
Designed around hands-on experience.
Future-focused
Digital and technology driven programmes.
Cranfield expertise
Part of world-leading University.
City of innovation
Located in one of UK's most forward-thinking cities.
Build talent. Drive growth. Future-proof your workforce.
Partner with MK:U, part of Cranfield University, to develop skilled employees through high-quality apprenticeships.