MSc Data Science and Artificial Intelligence

The MSc Data Science and Artificial Intelligence is a postgraduate taught degree. It is also a conversion course which is designed for students who do not have a computing undergraduate degree but who want to become experts in the field of artificial intelligence and data science. Graduates of this degree are likely to take up roles in industry and commerce as data scientists but could also progress to undertake PhD degrees perhaps using data science and AI in combination with the domain of their original undergraduate subject.

Course Aims

• 1. To enable students, regardless of their first degree subject, to gain essential computing knowledge and skills, enabling them to advance deeper into the AI and data science specialism.
• 2. To enable students to gain a deep comprehensive knowledge and systematic understanding of the advanced specialism of AI and Data Science.
• 3. To enable students to apply the theory in practice, designing and developing robust quality architectures and computational solutions.
• 4. To enable students to derive meaningful insights from those solutions with a due appreciation for the uncertainties and unknowns associated with those insights.
• 5. To ensure that students are fully aware of the ethical and privacy dimensions of AI and data science.

Learning Outcomes

• 1. Evaluate the technical dimensions of AI and data science architectures, solutions and techniques.
• 2. Demonstrate deep knowledge and systematic understanding of Artificial Intelligence and Data Science theory, techniques, tools and applications as informed by research and practice at the forefront of the discipline.
• 3. Combine theory, research and practice at the forefront of the discipline and use it to guide robust and highquality practical programming and computational solutions and architectures.
• 4. Understand the ethical and privacy dimensions of Artificial Intelligence and Data Science
• 5. Critically review literature, software and systems.
• 6. Possess system-level competencies in assessing, understanding, creating and improving AI and data science solutions and architectures.
• 7. Possess translational skills to communicate solutions and insights to technical and non-technical audiences.
• 8. Recognise and respond to opportunities for innovation.
• 9. Set goals and identify resources for lifelong learning.