1. Develop a foundation in AI, machine learning, and data science principles.
2. Empowers problem-solving and critical thinking skills for data-driven decision-making.
3. Foster innovation and entrepreneurship in AI and data science.
4. Prepare students for successful careers in technology, research, and industry.
5. Prepare graduates for leadership roles in AI, data science, and technology industries.

1. Two A-levels or equivalent.
2. Preferably in biology or related sciences.
3. Strong communication and interpersonal skills.
4. English language proficiency is required.
5. Desire for positive impact.
6. Solid interest in agriculture and animal welfare.
7. Work or volunteer experience in farming or animal care is beneficial.

1. Fundamentals of Computer Systems
2. Fundamentals of Software Engineering
3. Programming 0
4. Fundamentals of Networking and Cloud Computing
5. Maths for Computing
6. Database Development
7. Practical Computing
8. Programming 1
9. Object Oriented Analysis and Design
10. Human Computer Interaction
11. Programming 2
12. Web Application Development 1
13. Software Processes and Practice
14. Introduction to Data Science
15. DevOps
16. Group Project
17. Data Visualisation
18. Research Skills and Professional Issues
19. Programming 3
20. Advanced Data Science
21. Big Data and IoT
22. Cloud Platform Development
23. Machine Learning
24. Honours Project

1. Lectures
2. Seminars
3. Independent study
4. Personal tutors
5. Practical sessions
6. Laboratory work

1. Programming
2. Database Systems
3. Mathematics for Computing
4. Professional Perspectives
5. Imperative Programming Foundat
6. Computer Architecture & OS
7. Python Programming
8. Web Technologies
9. Team Project
10. Principles and Practice of Data Science
11. Mathematics and Statistics for Data Science
12. Industrial Projects
13. Business & Enterprise Prog
14. Applied Data Science ft Python
15. Data Structures & Algorithms
16. User Experience (UX) & HCI
17. Data Systems, Management & Eth
18. Industry and Community Engagement
19. Machine Learning
20. Deep Learning
21. Synoptic Project
22. High-Performance Computing and Big Data
23. Research Methods
24. Data Governance and Management
25. Mobile Computing
26. Research in Computing
27. User Experience and Interaction Design
28. Rapid Applied Problem Solving

1. Coursework
2. Written examination
3. Individual assignments
4. Group assignments
5. Class tests
6. Practical laboratory demonstrations
7. Individual verbal presentations
8. Group verbal presentations

This programme may vary depending on the institutions and countries, but the general standard options in the UK are:

1. 03 years (full-time).
2. 04 years with work placement (optional).

1. High-performance computing clusters
2. Data centres
3. Specialised laboratories
4. Access to cloud computing resources
5. Research collaboration opportunities
6. Library and research resources
7. Student support services
8. Virtual reality (VR) and augmented reality (AR) labs
9. Robotics labs
10. Internet of Things (IoT) labs
11. Cybersecurity labs
12. Data visualisation labs
13. AI ethics labs
14. Start-up incubators

1. Data Scientist
2. Machine Learning Engineer
3. Machine Learning Scientist
4. Machine Learning architect
5. Applications Architect
6. Data Analyst
7. Data Scientist
8. Data Architect
9. Infrastructure Architect
10. Data Engineer
11. Data Analyst
12. Software Developer
13. Cloud Engineer
14. AI Researcher
15. Business Intelligence Analyst
16. Product Manager
17. Technology Consultant

1. Tuition fees are £19,000 - £27,750 per year and may vary depending on the institution.
2. Scholarships, grants, and financial opportunities are available.
3. Government loan aid is available.

1. A Level: CCC or equivalent.
2. A Level in Maths or Computing.
3. English language requirements.
4. Strong communication and interpersonal skills.

1. Opportunities for practical experience in AI and data science projects.
2. Students can participate in internships and placements with tech companies, gaining real-world experience in AI and data science.

1. Certified Data Scientist (CDS)
2. Certified Machine Learning Engineer (CMLE)
3. Certified AI Engineer (CAIE)
4. Certified Big Data Professional (CBDP)
5. Certified Data Analyst (CDA)
6. Certified Ethical Hacker (CEH)
7. Certified Information Systems Security Professional (CISSP)
8. Certified Natural Language Processing (NLP) Specialist
9. Certified Computer Vision Specialist
10. Certified Reinforcement Learning Specialist
11. Certified Deep Learning Specialist
12. Certified Data Mining Specialist
13. Certified Data Scientist in Finance
14. Certified Data Scientist in Healthcare
15. Certified Data Scientist in Marketing
16. Certified Data Scientist in Retail

Typically, it takes twice a year (fall and spring), but may vary like:

1. Fall (September/October)
2. Spring (January/February)
3. Summer (May/June)

"UEL is the place of opportunities, challenges, knowledge and dreams. It shaped my future and gave my work life a new purpose, a much better one - to become a data scientist." Says - "Romina Novaku, AI and Data Science."