UNDERGRADUATE

University Year 1: Level 4 Diploma in Cyber Security (120 Credits)

Module 1: Introduction to Cyber Security (20 credits)

  • Fundamentals of cybersecurity principles and concepts.
  • Overview of threats, vulnerabilities, and risk management.
  • Grading: Distinction, Merit, Pass, Fail

Module 2: Network Security (20 credits)

  • Understanding network security protocols and practices.
  • Implementation of firewalls, intrusion detection systems, and encryption.
  • Grading: Distinction, Merit, Pass, Fail

Module 3: Secure Software Development (20 credits)

  • Secure coding practices and principles.
  • Identifying and mitigating common software vulnerabilities.
  • Grading: Distinction, Merit, Pass, Fail

Module 4: Cyber Threats and Defense (20 credits)

  • Analysis of various cyber threats (malware, phishing, DDoS).
  • Strategies for incident response and recovery.
  • Grading: Distinction, Merit, Pass, Fail

Module 5: Cryptography and Data Protection (20 credits)

  • Principles of cryptography and encryption algorithms.
  • Securing data in transit and at rest.
  • Grading: Distinction, Merit, Pass, Fail

Module 6: Legal and Ethical Aspects of Cyber Security (10 credits)

  • Legal frameworks and regulations related to cybersecurity.
  • Ethical considerations and privacy issues.
  • Grading: Distinction, Merit, Pass, Fail

Module 7: Professional Skills and Development (10 credits)

  • Enhancing communication, teamwork, and problem-solving skills.
  • Preparing for a career in the cyber security field.
  • Grading: Distinction, Merit, Pass, Fail

Module 8: Cyber Security Project (20 credits)

  • Practical application of cyber security concepts.
  • Designing and implementing security measures in a simulated environment.
  • Grading: Distinction, Merit, Pass, Fail

Assessment and Grading:

  • Distinction (70% - 100%)
  • Merit (60% - 69%)
  • Pass (40% - 59%)
  • Fail (below 40%)

Year 2: Level 5 Diploma in Cyber Security (120 Credits)

Module 1: Advanced Network Security (20 credits)

  • In-depth study of advanced network security protocols and technologies.
  • Intrusion detection and prevention systems, next-generation firewalls.
  • Grading: Distinction, Merit, Pass, Fail

Module 2: Cyber Threat Intelligence (20 credits)

  • Analysis of threat intelligence sources and indicators of compromise.
  • Threat hunting and analysis techniques.
  • Grading: Distinction, Merit, Pass, Fail

Module 3: Secure System Administration (20 credits)

  • Advanced techniques for secure system administration.
  • Hardening operating systems and managing access controls.
  • Grading: Distinction, Merit, Pass, Fail

Module 4: Penetration Testing and Ethical Hacking (20 credits)

  • Hands-on exploration of penetration testing methodologies.
  • Practical skills in ethical hacking and vulnerability assessment.
  • Grading: Distinction, Merit, Pass, Fail

Module 5: Cloud Security (20 credits)

  • Understanding cloud computing security challenges and solutions.
  • Securing data, applications, and infrastructure in cloud environments.
  • Grading: Distinction, Merit, Pass, Fail

Module 6: Incident Response and Recovery (20 credits)

  • Strategies and procedures for responding to cybersecurity incidents.
  • Crisis management, communication, and post-incident analysis.
  • Grading: Distinction, Merit, Pass, Fail

Module 7: Advanced Cryptography and Blockchain (10 credits)

  • In-depth study of advanced cryptographic techniques and algorithms.
  • Introduction to blockchain technology and its security implications.
  • Grading: Distinction, Merit, Pass, Fail

Module 8: Professional Development and Industry Trends (10 credits)

  • Keeping up with the latest trends and developments in cyber security.
  • Advancing professional skills for career growth.
  • Grading: Distinction, Merit, Pass, Fail

Module 9: Research Project in Cyber Security (20 credits)

  • Independent research on a specific cyber security topic.
  • Critical analysis, investigation, and presentation of findings.
  • Grading: Distinction, Merit, Pass, Fail

Assessment and Grading:

  • Distinction (70% - 100%)
  • Merit (60% - 69%)
  • Pass (40% - 59%)
  • Fail (below 40%)

Level 4 Diploma in Strategic Business Management (120 Credits)

1. Introduction to Strategic Management (20 credits)

  • Understanding strategic management concepts and frameworks.
  • Analyzing the external environment and internal capabilities.

2. Principles of Marketing (20 credits)

  • Fundamentals of marketing strategy, market research, and consumer behavior.
  • Creating marketing plans and promotional strategies.

3. Financial Management (20 credits)

  • Introduction to financial statements, budgeting, and financial analysis.
  • Managing working capital and investment decisions.

4. Business Law and Ethics (20 credits)

  • Legal principles and regulations affecting businesses.
  • Ethical considerations and corporate social responsibility.

5. Organizational Behavior (20 credits)

  • Understanding human behavior in organizations.
  • Leadership, motivation, teamwork, and organizational culture.

6. Research Methods and Data Analysis (20 credits)

  • Research techniques, data collection, and analysis methods.
  • Applying research findings to strategic decision-making.

Grading:

  • Distinction (70% and above)
  • Merit (60-69%)
  • Pass (50-59%)
  • Fail (Below 50%)

Level 5 Diploma in Strategic Business Management (120 Credits)

1. Strategic Planning and Implementation (20 credits)

  • Developing strategic plans and aligning them with organizational goals.
  • Implementing strategies and monitoring progress.

2. International Business Management (20 credits)

  • Managing businesses in a global context.
  • International market entry strategies, cultural considerations, and globalization.

3. Business Innovation and Entrepreneurship (20 credits)

  • Promoting innovation and fostering entrepreneurial thinking.
  • Identifying and evaluating business opportunities.

4. Project Management (20 credits)

  • Principles of project management, planning, execution, and control.
  • Managing project teams, resources, and risk.

5. Business Research Project (20 credits)

  • Independent research project focusing on a strategic business issue.
  • Data collection, analysis, and recommendations.

6. Leadership and Change Management (20 credits)

  • Leading organizational change initiatives.
  • Managing resistance to change, communication, and transformational leadership.

Grading:

  • Distinction (70% and above)
  • Merit (60-69%)
  • Pass (50-59%)
  • Fail (Below 50%)

Level 4 Diploma in Data Science (Year 1) - 120 Credits

Module 1: Introduction to Data Science (20 credits)

  • Fundamentals of data science and its applications.
  • Data collection, preprocessing, and exploration techniques.
  • Basic statistical concepts and data visualization.

Module 2: Programming for Data Science (20 credits)

  • Introduction to programming languages used in data science (e.g., Python, R).
  • Data manipulation, control structures, and functions.
  • Hands-on coding exercises and projects.

Module 3: Data Analysis and Visualization (20 credits)

  • Advanced data visualization techniques.
  • Exploratory data analysis (EDA) and statistical inference.
  • Using libraries like Matplotlib, Seaborn, and Plotly.

Module 4: Machine Learning Fundamentals (20 credits)

  • Introduction to machine learning concepts and algorithms.
  • Supervised and unsupervised learning.
  • Building and evaluating basic machine learning models.

Module 5: Database Management and SQL (20 credits)

  • Basics of relational databases and data modeling.
  • Structured Query Language (SQL) for data manipulation and querying.
  • Designing and implementing database systems.

Level 5 Diploma in Data Science - 120 Credits

Module 6: Advanced Machine Learning (20 credits)

  • In-depth study of machine learning algorithms and techniques.
  • Feature engineering, model selection, and hyperparameter tuning.
  • Advanced topics such as ensemble methods and deep learning.

Module 7: Big Data Analytics (20 credits)

  • Handling and processing large datasets using tools like Hadoop and Spark.
  • Distributed computing and parallel processing.
  • Extracting insights from big data.

Module 8: Data Ethics and Privacy (20 credits)

  • Ethical considerations in data science and AI.
  • Privacy regulations, data security, and responsible data usage.
  • Case studies and discussions on ethical dilemmas.

Module 9: Capstone Project (30 credits)

  • A substantial data science project applying skills learned in previous modules.
  • Problem formulation, data collection, analysis, modeling, and interpretation.
  • Presentation of findings and insights.

Module 10: Professional Development and Communication (10 credits)

  • Communication skills for data scientists.
  • Preparing reports, visualizations, and presentations.
  • Career readiness and job market insights.

Grading System:

  • Distinction: 70% - 100%
  • Merit: 60% - 69%
  • Pass: 50% - 59%
  • Fail: Below 50%
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