Learning Objectives

• Technical Proficiency

Students will develop strong technical skills in at least two programming languages and relevant data science frameworks, enabling them to effectively implement and evaluate machine learning models for various data types.

• Hands-on Application

Students will complete industry-relevant projects to simulate challenges faced in professional settings, and gain practical experience through internships or collaborative education opportunities with organisations, preparing them for hands-on real-world applications.

• Ethical Considerations

Students will complete coursework on ethical considerations in AI, addressing bias, fairness, transparency, and privacy, to be able to navigate and address ethical dilemmas in AI applications. Case studies or projects, will be applied to foster a responsible and ethical approach to AI.

• Adaptability to Emerging Technologies

Students will demonstrate familiarity with and the ability to use the latest tools and technologies in data science and AI, involving emerging trends such as edge computing or federated learning and ensuring they are equipped to adapt to the rapidly evolving field.