Structure of the Qualification

Total Credits: 120 ECTS
Modules: Five mandatory units and two optional units allow learners to specialise according to career interests.

Core Modules

Mandatory for All Routes

1. Managing Innovation and Change in Computing (20 Credits)
   Focuses on strategies for fostering innovation and managing technological changes within organizations.

2. Systems Development and User Experience (UX) (20 Credits)
   Teaches design methodologies that prioritize user experience, covering usability, prototyping, and iterative testing.

3. Implementing and Managing Cyber Security (20 Credits)
   Provides essential knowledge for managing organizational cyber security, including threat analysis and protection strategies.

4. Business Intelligence Systems (20 Credits)
   Examines data-driven decision-making, including data modeling, dashboard creation, and analytics.

5. Independent Project (20 Credits)
   A practical project where learners apply computing skills to a chosen area, demonstrating problem-solving and project management capabilities.

Optional Units

Select Two out of the following elective courses

• Agile Transformation (10 Credits)
  Introduces Agile methodologies for rapid project delivery, emphasising flexibility, team collaboration, and continuous improvement.

• Enterprise Resource Planning (ERP) Systems (10 Credits)
  Focuses on implementing and managing ERP systems to optimize organizational efficiency and resource management.

• Mobile Application Development (10 Credits)
  Covers the fundamentals of mobile app development, including UI/UX design, cross-platform development, and deployment.

• Data Analytics and Big Data (10 Credits)
  Explores data processing techniques and big data tools to analyze large datasets, identify trends, and support decision-making.

• Networking and Infrastructure Development (10 Credits)
  Focuses on designing and managing network infrastructures, covering both hardware and software aspects of connectivity.

• Internet of Things (IoT) (10 Credits)
  Examines IoT architecture, protocols, and applications, teaching learners to integrate IoT systems within existing infrastructures.

• Artificial Intelligence (10 Credits)
  Explores AI concepts, including machine learning algorithms and applications in automation and data analysis.

• Computer-based Research Methods (10 Credits)
  Teaches research methodologies relevant to computing, focusing on quantitative and qualitative data collection and analysis.

  
  

  
  

Discover the various pathways and course combinations available to tailor your learning experience and achieve your goals. Click here to explore more information on your options.*

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

Guidance on Elective Selection

Learners are encouraged to consult with the Student Advisor Office to choose electives that align with their career goals and maximise both academic and practical performance. The Student Advisor Office provides personalised guidance to ensure optimal elective selections for skill development and professional growth.