These optional resources will familiarise students to fundamental concepts in Computer Science:

1. TED Talks
2. Replit 100 Days of Code (Python)
3. CodeWars or LeetCode
4. ISCO Networking Basics

Tuesday 15 September

• 09:30 – 12:00
• Room DA020

IMPORTANT: Please bring your photo ID with you on this date to complete enrolment.

During induction week, you will be introduced to the programme, teaching team and expectations of university-level study while helping you feel part of a supportive learning community. Familiarity with essential technical tools, such as programming environments, online platforms and university systems, will enable you to begin coding and accessing resources from the outset.

At the same time, you will be guided in developing key study skills, including problem-solving, computational thinking, time management, writing, referencing and maintaining academic integrity, alongside the awareness of professional and ethical issues in computing, such as responsible technology use, security and data ethics.

Week beginning: 21 September 2026

• Monday 12:00 – 15:00
• Tuesday 14:00 – 17:00
• Thursday 09:00 – 12:00

Full details of your individual academic timetable, including Semester 2 timetable which may differ, will be available via your university email calendar after you have completed the pre-arrival task online and set up your student login.

3 hours of contact time per 20 credit module, a total of 9 contact hours per week.

Your transition into the world of Data Science begins with a strong suite of modules entailing statistical & mathematical methods for AI and Data Science, analyses of algorithms and big data analytics. The second semester will introduce the concepts of distributed data processing and natural language processing and prepare you with requisite research and project management skills which are imperative to analyse real-world problems and design effective solutions using contemporary tools and technologies.

Your journey culminates in an individual dissertation or project, where you’ll apply industry-style practices and explore the future of technology and research.

Dr. Adnan N. Qureshi

Dr. Adnan N. Qureshi received his Ph.D. from the Institute for Research in Applicable Computing (IRAC), University of Bedfordshire, UK. His research areas include bio-medical image and signal processing, computer vision, machine learning, optimization and autonomous systems. He is Programme Leader and Senior Lecturer of Computer Science.

Dr. Vincent Hall

Dr. Vincent Hall is currently working as Lecturer in Computer Science. He is an expert in Data Science and Machine Learning with a first UG masters degree in Physics and Astronomy (Leeds). His MSc was in the Physical Sciences – Life Sciences boundary (Warwick). His PhD entailed creating a Machine Learning method to help Chemists by estimating the structures of proteins with UV light scans. The latest version of this technology, now updated and added to by others, is licenced by Warwick Innovation to Pharma companies, and free for academic use.