Data Science and Artificial Intelligence

This module explores advanced artificial intelligence (AI) topics and applications, with a focus on deep learning and data science techniques. This includes data-driven approaches to analyse structured and unstructured data using neural networks and make decisions using data science frameworks and concepts. The module also covers techniques to process images, audio samples, unstructured text, data management and ethics.

You’ll study topics such as:

• Structured and unstructured data analysis
• Free text and human language data analysis
• Multimedia, image and video data analysis
• The ethics of AI and data management including use cases of data bias and machine learning model bias
• Artificial intelligence systems and solutions
• Testing, optimisation and evaluation of an AI project
• AI project proposal and management
• Present and demonstrate an AI project

This module will develop skills to effectively convey intricate artificial intelligence (AI) ideas to diverse audiences. Focused on enhancing presentation skills, the module places a strong emphasis on translating complex concepts into accessible explanations while fostering critical evaluation of peer-reviewed scientific publications.

You’ll study topics such as:

• Independent thought and learner autonomy
• Seminar presentations in the format of the 3MT (three minutes thesis)
• Research-based scientific communication
• Critical evaluation of research findings
• Effective presentations

This module will apply the technical knowledge and understanding developed across the course into an original research project. This will include critical and ethical analysis of literature, synthesising the findings and tailoring them to the specific context of their research projects using a suitable research methodology.

You’ll study topics such as:

• Critical and ethical review of the literature
• Selection and application of appropriate research tools, techniques, or methods
• Design and development of the artefact/prototype
• Testing and user evaluation
• Critical reflection – evaluating the project deliverables and project success/failure
• Legal, social, and ethical considerations in the design and development of computer-based systems
• Sustainable development and deployment

This module will explore foundational methodologies in data science and AI, tackling data management and analytics problems using various software tools and techniques to manage, explore, and model data.

You’ll study topics such as:

• Mathematics underpinning data science and AI principles and methodologies
• Use of appropriate technologies for data science and AI including financial and environmental sustainability of technologies
• Acquiring data sets
• Handling different types of data
• Data preparation and manipulation
• Data storage methods and ethics
• Statistical test and confidence intervals
• Supervised and unsupervised learning and dimension reduction techniques
• Evaluation of statistical and machine learning models and data science techniques

This module covers programming in Python and R, including key concepts of modern programming languages, such as object-oriented.

You’ll study topics such as:

• Fundamental programming concepts, such as variables, data types, operators, expressions, control structures, and loops
• Functions, methods, parameters, and arguments to organise and reuse your code
• Design and develop programmes using an algorithmic approach
• Data structures, such as lists, dictionaries, sets, and strings, to store and manipulate data
• Principles of object-oriented programming and implement classes, objects, methods, and inheritance
• Libraries and frameworks to create GUI, databases, or networking applications

This module develops skills to design and conduct rigorous research projects in computing.

You’ll study topics such as:

• Identify research problems: Explore current challenges and opportunities in computing to uncover relevant research areas
• Formulate research questions: Craft precise questions, aims, and objectives aligned with identified problems
• Review literature and artifacts: Critically analysing existing research to synthesize knowledge and identify gaps
• Propose research methods: Evaluate and select appropriate research techniques specific to your computing area
• Communicate research proposals: Craft comprehensive proposals that meet professional, legal, and ethical standards