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This module builds a solid programming toolkit for scientific problem-solving. Apprentices move from core Python and R syntax to essentials tools for professional software development such as version control, testing, containers and AI-assisted coding. The module lays the foundations for the computing and software engineering work that will be encounter in subsequent modules

This module teaches how to turn raw data into compelling evidence for driving business decision-making. Apprentices learn to take untidy, incomplete and multi-source datasets, and transform them through rigorous wrangling, exploratory analysis and statistical modelling. Equal weight is given to why as well as how: they will justify modelling choices, quantify uncertainty and test hypotheses, while being mindful of data quality and limitation. Apprentices will then translate technical findings into interactive dashboards and narrative visualisations that influence business decisions.

This module covers the core mathematical and statistical concepts necessary for data science, with a strong emphasis on intuition, visualisation and practical applications. Apprentices will gain a solid foundation in key areas such as calculus, optimisation, linear algebra, probability and statistical methods, which are essential for understanding and applying data science methods effectively.

This module covers key methods for managing data, with a focus on relational database systems, alongside the ethical, legal and security considerations of data management practices. Databases are a core component of modern data systems, underpinning the development, operation and governance of data workflows. Apprentices will develop foundational knowledge and practical skills in designing, implementing and querying relational databases. In addition to relational systems, the module provides a broader perspective on data management, including enterprise-scale data architectures such as data warehousing and lakehouse technologies. 

The module introduces the essential project delivery and professional skills that support the successful implementation of technical work. Apprentices learn to scope small analytics projects, gather requirements, choose an appropriate delivery approach (waterfall, Scrum or hybrid), estimate effort, map risks, set measurable success criteria, communicate with stakeholders and reflect on their professional growth. The module also provides an opportunity to do group work on a real-world project. By the end of the module, apprentices will be ready to manage a compact data science project from idea to impact, and to articulate how it contributed to organisational goals.  

Machine learning methods are central to data science, covering various techniques crucial for classification, analysis and prediction. This module provides a comprehensive exploration of both supervised and unsupervised learning techniques, along with an introduction to the basics of reinforcement learning and deep learning. As a rapidly evolving and highly influential discipline, machine learning is pivotal in driving innovations across various industries. The curriculum is designed to equip students with a thorough understanding of these foundational techniques, enabling them to apply advanced machine-learning methods to solve complex practical problems. Through this course, students will gain theoretical insights and practical skills essential for their career in data science. 

This module bridges the gap between data prototypes and production-ready software. Apprentices will practise clean code, API design and human-centred UX to deliver data-driven applications that work best for end-users. Emphasis is placed on robust software engineering methodologies, common design patterns, sustainable development, testing and ethical software practices. 

Many of today’s impactful data problems cannot fit on a single consumer level computer. This module delves into the intricacies of big data management and analytics, as well as cloud computing technologies, preparing apprentices to tackle complex real-world data challenges. Apprentices will explore cutting-edge technologies and methodologies for handling large-scale datasets, including Spark, Dask and Ray, and experiment with NoSQL engines. They will also get hands-on experiences with cloud services such AWS, learning how to weigh up, performance, costs and security trade-offs. 

DevOps, which is an evolution of agile software development approaches, has become the key industrial practice to allow technology organisations to deliver software and data applications rapidly, securely and reliably to customers. DevOps combines development and operations to achieve a fast flow of planned work into production, where its successful implementation means fewer problems experienced by customers and faster fixes to those problems. Through continuous learning, DevOps enables organisations to continuously improve their products and services. By providing a comprehensive and in-depth view of DevOps thinking and techniques, and opportunities to master the enabling software tools in the context of data science, this module teaches how to operate in, implement and lead the DevOps environments of today and the future. 

The module aims to develop leadership capability for managing complex, multi-team data science projects. Apprentices will explore advanced project management frameworks, coaching models and change-management strategies, while working collaboratively on a sizeable group project. The module emphasises communication, ethical awareness, and strategic planning and leadership, preparing apprentices for senior technical and managerial roles. 

This module explores advanced AI techniques and their integration into real-world data science workflows. Apprentices will investigate cutting-edge paradigms and applications such as computer vision, natural language processing, generative AI and reinforcement learning from human feedback. They will also critically assess responsible AI frameworks to ensure that the deployment of AI systems is ethical, fair, transparent and trustworthy. 

The final year data science project represents the culmination of everything learned across the apprenticeship programme and must be completed prior to the Gateway Review. It should be a work-based project defined and agreed upon by both the employer and the ºüÀêÊÓƵ. Apprentices will hone a wide range of professional skills, including scoping business questions, translating them to testable hypotheses, testing them, planning, time management, critical thinking, self-reflection, collaborating and collecting requirements from different stakeholders, communication and presentation skills. The project will inform the final report that needs to be produced as part of the End Point Assessment. 

The Apprenticeship Assessment (formerly known as End-point Assessment)  is a core requirement for the Data Scientist Degree Apprentice and carries the final 60 credits of the degree programme. The Apprenticeship Assessment comprises a knowledge test, report (based on a work-based project) and a professional discussion (informed by a portfolio). The formats of which are outlined in the for the standard. The Apprenticeship Assessment must be completed over a maximum total assessment time of 6 months after the apprentice has met the Apprenticeship Assessment gateway requirements. 

This module builds a solid programming toolkit for scientific problem-solving. Apprentices move from core Python and R syntax to essentials tools for professional software development such as version control, testing, containers and AI-assisted coding. The module lays the foundations for the computing and software engineering work that will be encounter in subsequent modules

This module teaches how to turn raw data into compelling evidence for driving business decision-making. Apprentices learn to take untidy, incomplete and multi-source datasets, and transform them through rigorous wrangling, exploratory analysis and statistical modelling. Equal weight is given to why as well as how: they will justify modelling choices, quantify uncertainty and test hypotheses, while being mindful of data quality and limitation. Apprentices will then translate technical findings into interactive dashboards and narrative visualisations that influence business decisions.

This module covers the core mathematical and statistical concepts necessary for data science, with a strong emphasis on intuition, visualisation and practical applications. Apprentices will gain a solid foundation in key areas such as calculus, optimisation, linear algebra, probability and statistical methods, which are essential for understanding and applying data science methods effectively.

This module covers key methods for managing data, with a focus on relational database systems, alongside the ethical, legal and security considerations of data management practices. Databases are a core component of modern data systems, underpinning the development, operation and governance of data workflows. Apprentices will develop foundational knowledge and practical skills in designing, implementing and querying relational databases. In addition to relational systems, the module provides a broader perspective on data management, including enterprise-scale data architectures such as data warehousing and lakehouse technologies. 

The module introduces the essential project delivery and professional skills that support the successful implementation of technical work. Apprentices learn to scope small analytics projects, gather requirements, choose an appropriate delivery approach (waterfall, Scrum or hybrid), estimate effort, map risks, set measurable success criteria, communicate with stakeholders and reflect on their professional growth. The module also provides an opportunity to do group work on a real-world project. By the end of the module, apprentices will be ready to manage a compact data science project from idea to impact, and to articulate how it contributed to organisational goals.  

Machine learning methods are central to data science, covering various techniques crucial for classification, analysis and prediction. This module provides a comprehensive exploration of both supervised and unsupervised learning techniques, along with an introduction to the basics of reinforcement learning and deep learning. As a rapidly evolving and highly influential discipline, machine learning is pivotal in driving innovations across various industries. The curriculum is designed to equip students with a thorough understanding of these foundational techniques, enabling them to apply advanced machine-learning methods to solve complex practical problems. Through this course, students will gain theoretical insights and practical skills essential for their career in data science. 

This module bridges the gap between data prototypes and production-ready software. Apprentices will practise clean code, API design and human-centred UX to deliver data-driven applications that work best for end-users. Emphasis is placed on robust software engineering methodologies, common design patterns, sustainable development, testing and ethical software practices. 

Many of today’s impactful data problems cannot fit on a single consumer level computer. This module delves into the intricacies of big data management and analytics, as well as cloud computing technologies, preparing apprentices to tackle complex real-world data challenges. Apprentices will explore cutting-edge technologies and methodologies for handling large-scale datasets, including Spark, Dask and Ray, and experiment with NoSQL engines. They will also get hands-on experiences with cloud services such AWS, learning how to weigh up, performance, costs and security trade-offs. 

DevOps, which is an evolution of agile software development approaches, has become the key industrial practice to allow technology organisations to deliver software and data applications rapidly, securely and reliably to customers. DevOps combines development and operations to achieve a fast flow of planned work into production, where its successful implementation means fewer problems experienced by customers and faster fixes to those problems. Through continuous learning, DevOps enables organisations to continuously improve their products and services. By providing a comprehensive and in-depth view of DevOps thinking and techniques, and opportunities to master the enabling software tools in the context of data science, this module teaches how to operate in, implement and lead the DevOps environments of today and the future. 

The module aims to develop leadership capability for managing complex, multi-team data science projects. Apprentices will explore advanced project management frameworks, coaching models and change-management strategies, while working collaboratively on a sizeable group project. The module emphasises communication, ethical awareness, and strategic planning and leadership, preparing apprentices for senior technical and managerial roles. 

This module explores advanced AI techniques and their integration into real-world data science workflows. Apprentices will investigate cutting-edge paradigms and applications such as computer vision, natural language processing, generative AI and reinforcement learning from human feedback. They will also critically assess responsible AI frameworks to ensure that the deployment of AI systems is ethical, fair, transparent and trustworthy. 

The final year data science project represents the culmination of everything learned across the apprenticeship programme and must be completed prior to the Gateway Review. It should be a work-based project defined and agreed upon by both the employer and the ºüÀêÊÓƵ. Apprentices will hone a wide range of professional skills, including scoping business questions, translating them to testable hypotheses, testing them, planning, time management, critical thinking, self-reflection, collaborating and collecting requirements from different stakeholders, communication and presentation skills. The project will inform the final report that needs to be produced as part of the End Point Assessment. 

The End Point Assessment (EPA) is a core requirement for the Data Scientist Degree Apprentice and carries the final 60 credits of the degree programme. The EPA comprises a knowledge test, report (based on a work-based project) and a professional discussion (informed by a portfolio). The formats of which are outlined in the  for the standard. The EPA must be completed over a maximum total assessment time of 6 months after the apprentice has met the EPA gateway requirements.