Learning Outcomes

• Demonstrate a foundational understanding of Artificial Intelligence concepts, including key principles of machine learning, neural networks, and intelligent systems.
• Apply computational thinking and basic algorithmic problem-solving techniques to solve real-world challenges within AI and data environments.
• Develop and execute simple programs using Python, demonstrating competence in core programming structures such as variables, loops, functions, and data structures.
• Understand and interpret data using basic data analysis techniques, including data collection, processing, visualization, and introductory statistical methods.
• Evaluate the ethical, legal, and societal implications of AI, including fairness, accountability, transparency, and the responsible use of technology.
• Apply basic mathematical concepts relevant to computing, such as logic, algebra, and statistics, to support learning in AI and data systems.
• Demonstrate effective academic and research skills, including critical thinking, academic writing, referencing, and independent learning strategies.
• Communicate technical ideas clearly and effectively, both verbally and in written form, to both technical and non-technical audiences.
• Develop confidence in using digital tools and technologies, preparing for further academic studies or entry-level roles in AI and data-related fields.
• Identify progression pathways into undergraduate degrees in AI, Data Science, Computer Science, or related fields, with a clear understanding of future career options in the technology sector.