Learn about the implementation of digital business models and technologies intended to realign an organisation with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.
Quantitative Data Analysis
Gain an understanding of the quantitative data analysis methods that underpin data science. You’ll learn compulsory methods in data science application and research like bi-variate and multi-variate methods, as well as regression. You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.
High Performance Computational Infrastructures
Work effectively with large-scale data storage and processing infrastructures, developing both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content covers, highly-scalable data-storage paradigms (e.g. NoSQL data stores) alongside cloud computing tools (e.g. Amazon EC2) and in-memory approaches.
Systems Project Management
Explore the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches, and the ways in which the state-of-the-art can be improved.
Big Data Analytics
Develop the reflective and practical approaches necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods, techniques, and algorithms for generating value and insight from real-time processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (e.g. clustering, regression, support vector machines, boosting, decision trees and neural networks).
An introduction to methods of data collection and analysis when conducting empirical research. You can choose to carry out this research in an organisational setting, both in the private or the public sector. This module is essential preparation for your dissertation.
Understand how to visually present insight drawn from large heterogeneous data sets (e.g. to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design, and augmented reality. An emphasis is also placed on visual storytelling and narrative development.
Learning Development Project
Research and develop a team-based integrative solution to a challenge drawn from the business, scientific and/or social domain. Working together, you’ll refine a coherent set of stakeholder requirements from an open-ended problem. You’ll then develop a solution addressing those requirements that draws upon the knowledge and skills of the other modules within the programme, and effectively evaluate the solution (with stakeholders where appropriate).
Your dissertation is an opportunity to showcase subject specific skills and project management to potential employers. It also serves as valuable experience and a solid building block if you wish to pursue a PhD after the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems, and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.
As preparation for the dissertation, you’ll be given a grounding in both quantitative and qualitative methods of data collection and analysis appropriate to conducting empirical and/or experimental research.