Level 5 Course Overview

This programme develops advanced technical and analytical skills for data‑driven roles. Learners build solid foundations in Python, mathematics, statistics, and databases before progressing to machine learning, big‑data analytics, visualisation, and professional practice.

Entry Requirements

  • Completion of a Level 3 Diploma in equivalent qualification.
  • Age: 21 or over before 31st December.
  • GCSE/GCE O Level (Grade A-C).

Credit Value

  • 20 Credits per course.
  • Total: 240 credits.

Duration

1-3 Years

Progression Route

Graduates can move on to Level 6 or undergraduate degree programmes in Data Science, AI, or Analytics, or enter roles such as Data Analyst, Junior Data Scientist, or Business Intelligence Developer.

  • 1. Programming with Python
  • 2. Linear Algebra and Calculus
  • 3. Introduction to Data Science
  • 4. Database Systems
  • 5. Statistical Methods for Data Science
  • 6. Machine Learning Foundations
  • 7. Data Visualisation and Communication
  • 8. Data Mining and Big Data Analytics
  • 9. Data Ethics and Privacy
  • 10. Advanced Data Analytics
  • 11. Data Science Capstone Project
  • 12. Professional Development in Data Science

Level 6 Course Overview

This advanced diploma hones high‑level data‑science expertise, combining deep‑learning techniques, cloud‑scale data engineering, and rigorous governance. Students master cutting‑edge tools for building, deploying, and securing data solutions, culminating in an applied capstone project.

Entry Requirements

  • Completion of a Level 5 Diploma in equivalent qualification.
  • Age: 21 or over before 31st December.
  • GCSE/GCE O Level (Grade A-C).

Credit Value

  • 20 Credits per course.
  • Total: 120 credits.

Duration

1-3 Years

Progression Route

Graduates may progress to Level 7 or master’s programmes in Data Science, AI, or Analytics, or pursue roles such as Data Engineer, Machine‑Learning Engineer, or Cloud Data Architect.

  • 1. Advanced Programming for Data Science
  • 2. Advanced Machine Learning and Deep Learning
  • 3. Cloud Computing and Data Engineering
  • 4. Data Engineering and Big Data Technologies
  • 5. Ethics, Governance and Data Security
  • 6. Capstone Project: Applied Data Science Solutions