Teaching blocks

Foundations in Maths for Data Science

This block 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.

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

Foundations in Software Development

This teaching block will introduce the basics of software development to learners with little or no previous experience. It covers basic principles of coding and will give 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.

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

Introduction to Probability and Statistics

This block introduces apprentices to probability, probabilistic reasoning and statistical inference. It will provide a good grounding in practical data analysis, and enable apprentices to use a computer package to apply the learned principles and methods.

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

Further Maths Methods for Data Science

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

Apprentices will 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 the apprentice will need to successfully manage and analyse data.

Using real-world datasets, standard software packages and data visualisation techniques, apprentices will 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 blocks

Software Portfolio Assessment

This assessment covers a progressive series of data science problems that require learners 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

This assessment addresses all 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

This assessment will develop 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 to demonstrates the acquisition and application of knowledge, skills and behaviours in line with the apprenticeship standard. 

Synoptic Data Science Assessment 1

This assessment will help learners remember consolidated knowledge from their first year of learning.

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

Teaching blocks

Statistical Models and Methods

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

Apprentices will acquire the knowledge and skills relevant to a data scientist.

Probability Models and Time Series

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

Apprentices will learn stochastic processes, and time series analysis, and there will be 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

This block 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 will be introduced to SQL, as well as alternative models such as NoSQL  databases. 

Responsible Decision Making

This block will introduce apprentices to the principles of ethical data-driven development of decision-making tools. It will cover topics such as data security and governance, privacy and data protection, ethics of AI, creating trustworthy algorithms based on fairness and diversity, as well as legal frameworks, codes of ethics and professional responsibility.

Apprentices will need to 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

This block provides apprentices with an understanding of data visualisation concepts, terminology, methods, and its importance in data processing. 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 will cover 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 will be encouraged to use visualisation tools used within their organisation to present findings in their role.

AI and Machine Learning

This block introduces a range of Artificial Intelligence and Machine Learning techniques.  The module will cover the history of AI and topics such as local search techniques, evolutionary algorithms, neural networks and deep learning.

It will prepare learners 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

The aim of this block is to enable 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 121 support via the apprentice’s Personal Tutor.

Assessment blocks

Data Science Group Project

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

In this assessment, apprentices will demonstrate their knowledge and skills associated with probabilistic 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 jobs.

Applied Machine Learning

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

This assessment is intended to help learners remember consolidated 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.

Teaching blocks

Scaling Up Data Science

This block aims to introduce apprentices to the concepts needed to deliver data science projects at scale, tackling problems which cannot be solved on a single computer. Apprentices will understand how to do this from a practical point of view as well as understanding the limitations of such approaches.

It will introduce 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 will cover 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 will 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 Module

This teaching block provides apprentices with an opportunity to develop independent planning and research abilities. It enables them to synthesise and further develop their project planning and writing skills gained in the programme.

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

Becoming a Professional Data Scientist

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

By introducing apprentices to relevant literature and professional networks, the aim is to enable them to prepare for further study, research and lifelong learning opportunities as they progress their career. They will 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 learners to proceed through the Gateway and will be used as a basis for the professional discussion part of the end-point assessment.

Big Data and Cloud Project

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.

Assessment block

Work based project

The final year project must be completed before the Gateway Review.  The project must be based on a significant workplace data science problem which will be scoped out and agreed between the employer and the University. 

Final six months       

End-Point Assessment (EPA) period

Gateway

To pass the Gateway Review and progress to the end point assessment, the apprentice must meet the following criteria:

• Meet all the knowledge, skills and behaviours of the apprenticeship standard
• Pass all the preceding degree/apprenticeship modules (300 credits)
• Complete a work-based project to inform their report
• Complete a portfolio as specified within the end-point assessment plan.

Employers must also be satisfied that the apprentice is consistently working at or above the level set out in the occupational standard. All criteria will need to be demonstrated to the end-point assessment organisation.

End Point Assessment