You will study 180 credits in total during your Advanced Computer Science (Data Analytics) MSc. A standard module is typically worth 15 credits and the research project is worth 60 credits. These are the modules studied in 2019. If you are starting in September 2020, these will give you a flavour of the modules you are likely to study. All Modules are subject to change.
Recent projects include:
- Text mining of e-health patient records
- Java-based visualisation on ultra-high resolution displays
- Data mining of sports performance data.
Machine learning - 10 credits
Topics selected from: Decision trees, Bayesian networks, instance-based learning, kernel machines, clustering, reinforcement learning inductive logic programming, artificial neural networks, deep learning.
Big Data Systems - 15 credits
The aim of the module is for students to develop a practical understanding of methods, techniques and architectures needed to build big data systems required, so that knowledge may be extracted from large heterogeneous data sets.
Data Science - 15 credits
The aim of the module is for students to understand methods of analysis that allow people to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.
Optional modules include:
Bio-Inspired Computing - 15 credits
Introduces the use of natural systems as the inspiration for artificially intelligent systems. This module covers the history, philosophy and application of bio-inspired computing, including swarm intelligence, neural networks and evolutionary design.
Knowledge Representation and Reasoning - 15 credits
The principal representations and algorithms used in machine learning and the techniques used to evaluate their performance. You will implement a challenging learning system using a publicly available pack of standard algorithms.
Parallel and concurrent programming - 15 credits
This module introduces you to the principles and practice of parallel and concurrent programming on shared memory architectures (both CPU and GPU). It covers the fundamental concepts underlying concurrency, in particular the complexity of managing shared resources and the language/data abstractions used to mediate interaction between threads of execution.
Data Mining and Text Analytics - 15 credits
Introduction to data and text theory and terminology. Tools and techniques for data-mining and text processing, focusing on applied and corpus-based problems such as data classification by Machine Learning classifiers, collocation and co-occurrence discovery and text analytics. Open-source and commercial text mining and text analytics toolkits. Web-based text analytics.
Cloud Computing - 15 credits
State-of-the-art approaches and solution strategies for designing, building and maintaining cloud applications. This module covers areas such as programming models, virtualisation and quality of service.