Diploma in Artificial Intelligence

Admission Requirements

• Typical Age: 18+
• Prior Qualifications:  A GCE Advanced level profile with achievement in 2 or more subjects, a Level 3 qualification in Data Science or a related field, or an Access to Higher Education Certificate from an approved institution. 
• International Qualifications: Equivalent qualifications may include:
  • High school diplomas or equivalent secondary qualifications in Asia, Africa, Europe, and the Americas.
• English Proficiency (for non-native speakers): IELTS 5.5, CEFR level B2, or equivalent​.
• Additional Guidance : For assistance with qualification equivalency or language requirements, please contact our admissions office.

Structure of the Qualification

• Total Credits: 120 ECTS
• Modules: 6 mandatory units, each worth 20 credits, providing a broad introduction to artificial intelligence concepts and practices.

Core Modules

1. Introduction to Artificial Intelligence and Applications (20 Credits): Introduces AI principles, models, technologies, and industry applications, including how AI can address future sustainability needs.
2. Mathematical Foundations for Machine Learning (20 Credits): Covers probability, statistics, hypothesis testing, regression, and calculus, with application to business problems.
3. Data Science Using Python (20 Credits): Teaches Python and key libraries for data manipulation, visualisation, exploratory data analysis, and basic machine learning.
4. Big Data Management (20 Credits): Explores big data technologies such as Hadoop and NoSQL, along with data mining, visualisation, industry applications, and ethical implications.
5. Introduction to Deep Learning (20 Credits): Explains neural networks and key deep learning concepts, including applying and evaluating models for image classification.
6. Artificial Intelligence Ethics (20 Credits): Examines ethical frameworks, bias, privacy, security, governance, and global approaches to AI regulation.

Explore the complete curriculum for this course to gain a detailed understanding of the topics covered, learning objectives, and key skills you'll develop. See the full curriculum below.

View Curriculum

Assessment and  Regulations 

Assessment Approach

Assessment is based on a combination of exams, assignments, and project evaluations, aligned with each unit’s learning outcomes. Assessments include:

• Written Assignments: Assessing comprehension and application of AI principles, using examples from the workplace, case studies, or research.
• Exams: Testing learners’ understanding of key AI concepts.
• Projects and Case Studies: Practical tasks that relate to real-world AI issues, requiring critical thinking and problem-solving.

Grading System

Grades are awarded based on the successful completion of unit-specific assessment criteria:

• Distinction: 70% and above
• Merit: 60%–69%
• Pass: 40%–59%
• Fail: 0%–39%

Completion Requirements

Learners must pass all units by meeting all required learning outcomes and assessment criteria. All assessments are marked internally by the approved centre and externally moderated by Awarding Body to ensure quality and fairness.

Professional Development and Progression

Progress to Higher-Level Study:

• Advance to our progression course, the Level 5 Diploma in Artificial Intelligence.

Employment Opportunities:

This qualification prepares learners for entry-level AI and data roles, such as:

• Junior AI Developer
• Junior Data Scientist
• Junior Machine Learning Engineer
• Data Analyst

Work-Based Learning:

Practical tasks and case studies within each module support skill development and readiness for the workplace. Where possible, learners are encouraged to draw on workplace opportunities for research and investigation.

**When you enrol in our Level 4+5 bundle, you’ll start on the same course pathway for both levels. The possible pathways shown here are for reference only. If you wish to change your Level 5 route after registration, just reach out to Student Services—we’re here to guide you toward the best fit.