Taught modules

Foundations in Maths for Data Science

This module provides apprentices with the mathematical skills, confidence and competence in a range of fundamental elementary mathematical techniques. It also provides a basis for the advanced mathematical methods used to study and analyse data science problems.

Apprentices who can evidence an A in A-level Maths may be exempt from this module.

Foundations in Software Development

The module introduces the basics of software development to apprentices with little or no previous experience. It covers basic principles of coding and gives apprentices the technical skills to break down simple problems and produce solutions using software.

The focus will be on software that processes data and applies mathematical and data science techniques.

Apprentices who evidence strong previous experience in software development may be exempt from this module.

Introduction to Probability and Statistics

Apprentices are introduced to probability, probabilistic reasoning and statistical inference in this module. It provides a good grounding in practical data analysis and enables apprentices to use a computer package to apply the learned principles and methods.

Apprentices gain the knowledge and skills of relevance to a professional statistician.

Further Maths Methods for Data Science

The skills learnt in this module are applied throughout the rest of the programme to model data science problems within an organisation.

Apprentices consolidate core mathematical topics in the differential and integral calculus of a function of single variable. The module will extend basic theory to more advanced topics in the calculus of several variables to model real world scenarios that are multi-dimensional.

Introduction to Software Development for Data Science

This learning builds on the basic principles of programming and algorithms and addresses some of the key concepts that apprentices will need to successfully manage and analyse data.

Using real-world datasets, standard software packages and data visualisation techniques, apprentices learn how to organise and analyse data to answer questions about the world, as well as develop an appreciation of user needs surrounding data systems. 

Assessment modules

Software Portfolio Assessment (40 credits)

This assessment covers a progressive series of data science problems that require apprentices to implement software solutions. It gives apprentices the opportunity to draw on the teaching and formative activities to demonstrate their knowledge and develop their data science solutions skills.

Fundamental Skills Assessment (20 credits)

This assessment addresses all knowledge, skills and behaviours (KSBs) across the curriculum to identify evidence of apprentices’ learning from their first year on programme. It will assess their ability to analyse and reflect on their learning, to recognise and evaluate their own progress, and to present evidence of their achievement relevant to their employer demands.

Data Analysis Portfolio (40 credits)

This assessment develops the apprentices’ probabilistic reasoning and skills in collecting, analysing and organising data and information using relative statistical techniques.

The portfolio assessments across this programme provide an opportunity for apprentices to understand how to start structuring a portfolio of evidence, demonstrating the acquisition and application of knowledge, skills and behaviours in line with the apprenticeship standard. 

Synoptic Data Science Assessment 1 (20 credits)

In this assessment, apprentoces consolidate knowledge from their first year of learning.

Questions for this assessment may require apprentices to draw from their on-the-job experience as well as the academic programme.

Taught modules

Statistical Models and Methods

Apprentices are introduced to a wide range of statistical concepts and methods fundamental to applying statistics in data science. The module also covers key concepts and theory of linear models, illustrating their application via practical examples drawn from real-life situations.

Probability Models and Time Series

The ideas of probability introduced in the first year are extended in this module to include continuous random variables.

Apprentices learn stochastic processes, and time series analysis, and there is a particular focus on discrete-time Markov chains and forecasting methods that are fundamental to the wider study of techniques required to analyse probabilistic and statistical models to understand business processes. 

Databases

Databases gives apprentices a broad overview of the concepts, practical skills and applications of databases, including core ideas of what a database is, how to design a high-quality data model, and how to insert, modify, process and extract information. Apprentices are introduced to SQL (Structured Query Language), as well as alternative models such as NoSQL databases. 

Responsible Decision Making

This module introduces apprentices to the principles of ethical data-driven development of decision making tools. It covers topics such as data security and governance, privacy and data protection, ethics of Artifical Intelligence (AI), creating trustworthy algorithms based on fairness and diversity, as well as legal frameworks, codes of ethics and professional responsibility.

Apprentices investigate and understand specific legal and governance issues relevant to their role, pulling from, working with and enhancing their on-the-job knowledge and experience.

Visualisation Techniques

Apprentices are provided with an understanding of data visualisation concepts, terminology, methods, and its importance in data processing within this module. It enhances human perception and cognition to make sense of data in a way that effectively communicates conclusions drawn from the data to a wider audience.

The learning covers the challenges associated with visualising large, ambiguous or time-based datasets, to the psychological theories that help explain how humans process information and the relevance to the design of effective visualisations.

Apprentices are encouraged to use visualisation tools used within their organisation to present findings in their role.

AI and Machine Learning

AI and machine learning (ML) techniques are introduced in this module. It covers the history of AI and topics such as local search techniques, evolutionary algorithms, neural networks and deep learning.

The module prepares apprentices for further independent work on selecting appropriate techniques and developing their understanding and application of AI and ML to solve practical problems in data science.

Team Working Skills

This module enables apprentices to understand the basics of running an agile software development team, and how to apply this knowledge and skills in their academic software projects and in the workplace. This will be delivered alongside the second-year group project and includes one-to-one support via the apprentice’s Personal Tutor.

 Assessment modules

Data Science Group Project (40 credits)

This project enables apprentices to tackle a significant data science problem/s and follow the full data science pipeline.

Apprentices will work in groups of 3-6 people over the course of the module, supported by an academic supervisor. They will be required to identify and develop a computer application that relates to the work context and involves the use of programming and AI for a data science application.  At the end of the project, they will deliver a written report, and demonstrate the software as part of a face-to-face project presentation/wrap-up session. 

Statistics and Probability Modelling (40 credits)

In this assessment, apprentices will demonstrate their knowledge and skills associated with probability and statistical modelling by conducting applied statistical research and forecasting methods.

Apprentices will be encouraged to relate their outputs to areas of data science applicable to their current role.

Applied Machine Learning (20 credits)

This assessment block provides apprentices with the opportunity to apply AI and machine learning to data science problems. It includes three coursework elements of increasing complexity.

Apprentices will be encouraged to look for opportunities, data or business questions within their organisation to apply the techniques learned.

Synoptic Data Science Assessment 2 (20 credits)

This assessment is intended to help learners consolidate knowledge from their second year of learning. It will be delivered in two parts through a multiple-choice knowledge test and an open paper which poses higher level questions to data science learners.

The paper will include a reflective piece on the learner’s progress towards the KSBs, enable them to demonstrate their approach to their own professional development, and to foster an ongoing positive attitude to their development beyond their apprenticeship journey.

Taught modules

Scaling Up Data Science

The module introduces apprentices to the concepts needed to deliver data science projects at scale, tackling problems which cannot be solved on a single computer. Apprentices learn how to do this from a practical point of view as well as understanding the limitations of such approaches.

It introduces big data and the main principles behind distributed/parallel systems with data intensive applications, as well as how to identify the key challenges to capture, store, search, analyse and visualise the data.

The learning covers big data frameworks and how to deal with big data using a range of models and technologies, such as the MapReduce programming model, Hadoop ecosystem, and Apache Spark. Apprentices also dive into data mining and machine learning, including data preprocessing approaches (to obtain quality data), distributed machine learning algorithms and data stream algorithms.

Project Support

Apprentices are provided with an opportunity to develop independent planning and research abilities. It allows them to develop their project planning and writing skills gained within the programme.

Content is supported through online workshops and individual supervision tutorials. Group online sessions provide opportunities to assess progress and share ideas in a team environment.

Becoming a Professional Data Scientist

This module is designed to help apprentices prepare for work as a professional data scientist.

By introducing apprentices to relevant literature and professional networks, it sets them develops them for further study, research and lifelong learning opportunities as they progress their career. They build a portfolio of projects representing the development of their individual knowledge, skills and behaviours as well as reflecting and adapting their current CV to identify gaps and plan personal development targets for successful ongoing achievement. 

This portfolio will be necessary for apprentices to proceed through the gateway and will be used as a basis for the professional discussion element of the end-point assessment.

Assessment modules

Big Data and Cloud Project (20 credits)

This assessment will allow apprentices to learn how to build distributed big-data solutions. Over the module, apprentices will face progressively more difficult assessments, enabling them to consolidate their learning, build their skills and demonstrate their creativity in implementing solutions.

Where possible, apprentices are encouraged to use relevant projects from their workplace that require the use of high performance computing and techniques to address big data problems.

Work-based project (40 credits)

The final year project must be completed before the Gateway Review. The project must be based on a work-based project which will be scoped out and agreed between the employer and the University. 

Once the apprentice has completed all their on-programme learning, a meeting will take place between their employer and the university. During this meeting, the apprentice’s knowledge, skills and behaviours will be assessed to determine whether they have met the minimum requirements set out in the Data Scientist Degree Apprenticeship standard. Apprentices deemed to have met these requirements will progress onto the end-point assessment (EPA).

The final part of the apprenticeship is the end-point assessment. The end-point assessment requires apprentices to demonstrate that their learning can be applied in the real world. Apprentices will undertake a knowledge test, submit a report on their work-based project and take part in a professional discussion assessment.