Digital and Technology Solutions Specialist Degree Apprenticeship

The apprentice undertakes a major, in-depth, individual study in an aspect of computing, IT, computer science or digital technology. The Capstone Project is drawn from commercial, industrial or research-based problem areas. They research and investigate aspects of a specific computing discipline and then produce a major deliverable (software package or tool, design, prototype, website, model, research findings, results of an experiment, datasets). The apprentice carries out a critical evaluation of their major deliverable, including obtaining third party evaluation where appropriate.

Digital transformation is a change process that allows businesses and society to achieve goals such as improving accessibility and quality of digital services, increasing income generation, implementing innovative and generating a collaborative culture through the use of technology. The adoption of emerging technologies is important to businesses and can be fundamental to their survival. But digital transformation is not all about technology.

You gain an in-depth understanding of the context of digital transformation, its challenges and possible future as well as its leadership and management. You gain the skills to develop a digital transformation strategy for an organisation, by engaging in critical assessment of cutting-edge research findings to inform your solution.

You develop the knowledge and skills to understand the research process in the field of computing and gain the necessary skills to undertake your masters project. You learn how to evaluate previous academic research and generate evidence material to justify your research. You learn different methods of data generation and develop an understanding of how these methods fit into your primary research, development lifecycle, evaluation of the end user experience, use of academic research literature and research ethics.

You gain experience of application development utilising an agile development approach. You also take responsibility for determining project aims, objectives, roles, tasks, deliverables, schedules and documentation.

By learning agile principles and applying a specific agile approach (eg SCRUM) in a live simulation of the methodology, the module will help you to prepare for contemporary practice in industry, developing both knowledge and skills in the application of agile principles.

This module explores in detail an established and widely recognised governance standard method called PRINCE2®. Students will work on simulated project brief in order to build their understanding and critically review the governance methodology.

The module will focus on the structure, processes, documentation, terminology but also tailoring of the governance method in order to plan for a particular project.

The students will be assessed by two in-course assessment elements (60% and 40%) requiring them to work as part of a team.

PRINCE2® is a registered trademark of AXELOS limited

Contemporary project management is an evolving and extensive discipline that has grown substantially to meet the needs of modern project management demands. This module cultivates detailed and critical awareness of this domain and its expanding range of philosophies, tools and frameworks.

The purpose of this module is to introduce students to the expanding discipline of project management and familiarise them with modern and contemporary project management modalities and the importance of effective project management to organisational functioning, enterprise growth and development.

We provide you with an advanced level of study in risk management in projects. You explore a range of tools and techniques used in risk identification, qualitative and quantitative risk analysis, risk-planning responses, PERT and risk monitoring. You learn about financial risk, project-appraisal methods and the application of a decision tree within a project.

You gain awareness of probability theory that represents the corner stone of risk management. Invited speakers from the industry give an overview of risk management in different projects; for example IT, finance, construction, and oil and gas. You work in groups on project case studies which apply risk management theory. In addition, you gain hands-on experience in applying the @RISK© tool to a variety of scenarios of risk factors. You develop a deep understanding of the systematic process of risk management and application of @RISK© software.

You develop your ability to design and implement database, big data and analytics applications to meet business needs. A case study is used to follow the system development lifecycle. You develop a plausible application from inception to implementation for a real-world scenario.

You investigate the issues and technologies associated with implementing and supporting large scale databases and the services that are needed to maintain and access a repository of data. Investigations are undertaken in a number of areas including big data, data warehouses, integrating legacy data, data management and approaches that support the modelling and visualisation of data for a range of use views.

The field of information visualisation has expanded rapidly with many designers generating new forms of charts through which to view quantitative data. This module explores the range of charts available from the traditional such as bar charts and pie charts, to the more novel such as stream graphs, tree maps, sunbursts, and force diagrams, and examines their mathematical properties.

By accurately representing quantitative data using appropriate charts, the intended audience can make their own interpretations of the data and identify emerging patterns and themes that are more readily recognisable in chart form than in the form of raw data.

Machine learning is a subfield of computer science concerned with computational techniques rather than performing explicit programmed instructions. You build a model from a task based on observations in order to make predictions about unseen data. Such techniques are useful when the desired output is known but an algorithm is unknown, or when a system needs to adapt to unforeseen circumstances.

You explore statistics and probability theory as the fundamental task is to make inferences from data samples. The contribution from other areas of computer science is also essential for efficient task representation, learning algorithms, and inferences procedures. You gain exposure to a breadth of tasks and techniques in machine learning.

Assessment is an in course assessment (100%).

You develop necessary knowledge and practical understanding of the main statistical techniques. You explore quantitative and qualitative data analysis techniques, reflecting scientific and social science methods. You focus on correlation testing, regression, data categories, normalization - the tools needed, rather than the philosophical approaches. You understand how to apply valid techniques and interpret the results in preparation for experimental work.

Your assessment is a single ICA based around a number of case studies that require you to identify the correct data analysis and modelling processes.