Year 1
Introduction to Artificial Intelligence (Requisite)
This module provides an introduction to the artificial intelligence field, covering the history of the discipline and exploring the breadth of the discipline from “classical AI” to the current forefront areas. It provides a grounding in how to undertake research in AI and data science, and considers ethical issues arising in AI and data science applications
Python Programming for AI and Data Science (Requisite)
Programming is a core skill throughout computing. This module will cover Python programming with particular emphasis on using Python to solve problems with AI and data science techniques. No programming experience will be assumed. The module will begin with the key elements of Python programming and build towards harnessing the standard Python libraries and packages to create solutions. Best practices of Python coding will be embedded throughout the module. The module will also provide a primer on software engineering of solutions, with an emphasis on the importance of testing.
SQL and NoSQL Databases (Requisite)
Industry, commerce and research are being transformed by the potential to capture, store, manipulate, analyse and visualise data and information on a massive scale. Relational (SQL) databases and data warehouses remain important repositories of information to many organisations. The advent of Big Data with its variety, velocity and volume has challenged relational databases, leading to the emergence of NoSQL databases. Yet the query languages of NoSQL databases have evolved closer to SQL capabilities. This module will cover both SQL and NoSQL approaches to data modelling, database design and manipulation, so that you can use the right tool for the right job.
Data Mining and Statistical AI (Requisite)
Data science and artificial intelligence includes many techniques for classification, analysis and prediction. This module focuses on those techniques relating to data mining and statistically driven approaches, providing you with an arsenal of methods to solve business problems and generate real insights.
Deep Learning Techniques and Tools (Requisite)
Deep learning is central to modern AI. A sufficiency of inexpensive computing power, sufficiently large datasets and a number of key theoretical advances created deep learning techniques which have facilitated a wave of accuracy increases across many computational tasks (computer vision, natural language processing, speech recognition, autonomous driving, etc.), making many applications practical. This module explains the underlying mathematics and techniques, so that you can master deep learning and solve real problems.
Cloud Computing for AI and Data Science (Requisite)
The on-demand delivery of compute, database, storage, applications and IT resources through cloud computing has enabled many organisations to deliver innovative solutions without upfront capital investment. Cloud computing ecosystems provide a variety of scalable AI and machine learning solutions. This module provides a comprehensive grounding in cloud computing concepts and solutions, buttressed with extensive practicals to build experience in individual services and architectural designs. As the University of Suffolk is an AWS Academy partner institution, the module will give you an opportunity to acquire AWS certification(s) if you so wish.
Extended Project (Mandatory)
The Extended Project is the culmination of the MSc Data Science and Artificial Intelligence conversion degree. This project is your opportunity to apply the knowledge and skills acquired from all the earlier modules on a real task – it is very likely a project proposed by a company or research organisation.
