Advanced Data Science and Artificial Intelligence MSc
The Advanced Data Science and Artificial Intelligence MSc is an advanced programme of study delivered in partnership with the Royal University for Women, Kingdom of Bahrain. This MSc enables you to develop IT solutions to big data problems in a sector with a significant skills shortage. Gain hands-on experience of big data analytics, data mining and visualisation techniques using high-performance computer technology.
Introduction
Big data is increasingly important in the contemporary business and IT world. For many public and private enterprises, analysis of large-scale data sets is critical to growth.
This MSc will prepare you for employment in the IT industry, where big data professionals are in high demand.
You’ll learn how to interrogate vast amounts of structured and unstructured data and make informed insights from datasets that are too large to be readily processed using standard techniques.
We’ll provide an overview of the key algorithms, algorithmic approaches and software environments you’ll use when solving big data problems and explore data mining techniques.
Hands-on programming experience with the latest multi-core and multi-processor platforms will ensure your expertise in big data is underpinned by knowledge of high-performance computing. Further opportunities to specialise and enhance your knowledge of algorithms, optimisation and machine learning are available through a range of optional modules.
You’ll work as part of a small group on a practical project to find a solution to a big data problem. We’ll also provide a thorough grounding in how to plan and conduct research in preparation for your dissertation.
Who is this course for?
This programme is suitable for graduates with a degree in computer science or a closely related subject.
Please note: this version of the programme is delivered in partnership with the Royal University for Women, Kingdom of Bahrain.
Students wishing to study in Liverpool should visit the Computer Science website.
What you'll learn
- Theoretical and practical aspects of programming for the latest multi-core and multi-processor platforms
- Key algorithms, approaches and software environments for developing solutions to big data problems
- Data mining techniques and challenges using real-world datasets
- Application of visualisation methods to data mining
- Research skills in computer science
- Bio-inspired algorithms for optimisation and machine learning
- How to model continuous and discrete optimisation problems
Course content
The programme is divided into three equally weighted semesters. The first two comprise taught modules to a total of 60 credits per semester. A research-based project, culminating in a dissertation, is undertaken full-time over the summer period. This counts for a further 60 credits, making a total of 180.
Semester One Compulsory Modules
Multi-Core and Multi-Processor Programming – COMP828 (15 Credits)
This is a module to cover theoretical and practical aspects of parallel programming for multi-core architectures, with the main focus on hands-on programming experience with the latest multi-core and multi-processor platforms.
Optimisation – COMP857 (15 Credits)
This module is an in-depth tour of optimisation methods applied to various optimisation models. These methods are extensively used in both academic and industrial practices.
Big Data Analytics – COMP829 (15 Credits)
This module provides an initial overview of key algorithms and algorithmic approaches and corresponding software environments used when developing solutions to Big Data problems. It explains how to use these to analyse data. A significant portion of statistics, some advanced AI approaches, as well as key deterministic and hybrid algorithms are included to support the development of future data analytics and to understand how to develop stochastic, machine learning and hybrid algorithms that can exploit Big Data and can be applied to solve real-life problems.
Research Methods in Computer Science – COMP816 (15 Credits)
In this module the students will learn and practise all the necessary skills needed to conduct independent research in computer science, including literature search, project management, presentation techniques, peer reviewing, writing skills and critical review of texts. They will also learn about the professional, legal, social and ethical framework of the IT industry. The module covers, e.g., planning and scheduling projects and drawing Gantt charts. Students shall also conduct a research project (including research, paper, literature review, or MSc project proposal, …) and use tools like EndNote and Zotero bibliography manager within MS Word and Latex.
Semester Two Compulsory Modules
Data Mining and Visualisation – COMP827 (15 Credits)
The module covers a range of topics and techniques for analysing data. Students will learn about various types of data mining problems, including classification, clustering, association pattern mining, and social network analysis, as well as the algorithms used to solve them. Students will program selected data mining algorithms from scratch using Python. This hands-on approach will allow them to gain a deeper understanding of how the algorithms work and how they can be applied to real-world datasets. They will experiment with different datasets to see how the algorithms perform and learn how to interpret the results.
Machine Learning and BioInspired Optimisation – COMP832 (15 Credits)
This module teaches you about bio-inspired algorithms for optimisation and machine learning. The algorithms are based on reinforcement learning, DNA computing, brain or neural network models, immune systems, the evolutionary version of game theory, and social insect swarm behaviour, such as ant colonies and bee colonies. These techniques are extremely useful for searching very large solution spaces (optimisation) and they can be used to design agents or robots that have to interact and operate in dynamic unknown environments (e.g. a Mars rover, a swarm of robots or a network of satellites). The idea of learning optimal behaviour, rather than designing algorithms and controllers, is especially appealing in AI.
MSc Group Project – COMP830 (15 Credits)
This module is designed to allow students to consolidate work from the first semester by working as a programming team to realise a solution to a problem related to their programme of study.
Computational Intelligence – COMP875 (15 Credits)
Biologically inspired optimisation and introduction to neural networks for artificial intelligence.
Semester Three Compulsory Modules
MSc Project – COMP802 (60 Credits)
COMP802 is the final project module that will run after semesters 1 and 2. The aim of the project is for a student to develop and demonstrate autonomy in the management and development of a realistic project in computer science, either research or application-oriented. In either case, the project should have some degree of scholarly added value to it. Delivery and assessment of the project includes a final dissertation describing the project as a whole and including a critical evaluation of both the conduct and the outcome of the project.
Careers and employability
Designed to address a skills gap in the employment market, this MSc will enable you to apply your skills working with big data and your knowledge of high-performance computing to real-world challenges.
Potential roles working with big data include:
- Data analyst
- Data scientist
- Mathematical modeller
- Database administrator
- Machine learning engineer
- Statistician.
Entry requirements and how to apply
Visit the Royal University for Women website for application information