Best Aspects Strong academic support: Lecturers and TAs are approachable, and lab sessions help a lot for modules in Data Science and AI. Practical learning: Coursework and assignments focus on real-world skills, which makes the programme feel relevant and hands-on. Great campus environment: The library, study spaces, and overall facilities are excellent for both group and independent work. Active student community: Societies like COMPSOC, Indian Society, Badminton, Board Games, and Snooker helped me settle in and meet people easily. Supportive atmosphere: The Guild and university events create a sense of belonging and make student life enjoyable. Worst Aspects / Areas to Improve Communication can be clearer: Sometimes assessment expectations or instructions feel unclear, especially for technical modules. Fast-paced modules: Additional optional tutorials, workshops, or coding support would benefit students new to certain topics. More career-related opportunities: More industry talks, networking sessions, and internship guidance specific to Data Science and AI would be helpful. Overall A very positive learning environment with strong academic and social support, and with a few improvements in communication and academic pacing, the experience could be even better.

Best Aspects Well-structured teaching: Modules are taught clearly through a mix of lectures, labs, and practical sessions, which helps in understanding complex AI and data science concepts. Supportive lecturers and TAs: Contact hours and lab support sessions are very helpful, especially when working through programming tasks or mathematical concepts. Hands-on learning: Most modules include practical coding exercises, real datasets, and project-based assessments that build confidence and industry-relevant skills. Good learning resources: Canvas materials, lecture recordings, and example solutions make it easier to revise and study at your own pace. Blended learning flexibility: Having both in-class teaching and online materials allows students to manage their workload more effectively. Worst Aspects / Areas for Improvement Fast-paced modules: Some topics move quickly and can feel intense, especially for students without a strong programming or mathematical background. Clarity in assessments: Instructions for coursework could be more detailed or consistent, as expectations sometimes feel unclear. Limited interaction time: Additional optional workshops or weekly guidance sessions would help reinforce difficult topics. More industry exposure: More guest lectures, career-focused workshops, or internship pathways tailored to AI/Data Science would strengthen the practical side of the course. Overall A strong and well-taught course with helpful staff and practical learning, but a bit more structured support and clearer guidance in assessments would make the experience even better.

I really like it but I feel lost sometimes

A lot of the assignments for subjects this term have nothing to do with the actual content, a lot of the time i learn something in the lecture and it doesn’t apply at all to what the assignments are.

Yeah good but more fridge space is neneeed