Diploma Programme
Diploma in Data Science & Analytics
A three-year, 360-credit pathway for learners who want to turn data into insight and decision-making power.
Data Science & Analytics
Transform data into meaningful insight.
Every click, transaction, customer interaction, and business operation generates data. This diploma prepares learners to turn raw information into clear business intelligence and practical decisions.
Students work with statistics, artificial intelligence, machine learning, big data technologies, cloud computing, advanced analytics, real datasets, and workplace-style projects.
What you will learn
Learning outcomes shaped for employability.
Data foundations
Collect, clean, analyse, visualise, and interpret complex datasets for real organisational problems.
Business intelligence
Build dashboards, reports, and insight narratives that support strategic decision-making.
Applied AI and ML
Use machine learning and artificial intelligence methods with practical datasets and case studies.
Cloud and big data
Understand modern data platforms, cloud-based workflows, and big data technologies.
Research confidence
Apply statistical reasoning, data governance, privacy, and professional communication.
Career readiness
Graduate with the technical and analytical confidence required for data-focused roles.
Programme Structure
Semester-wise syllabus
Open each semester to review the subjects and associated credits. The full fee schedule remains available as a downloadable document.
Semester 1 52 credits
Syllabus
- Introduction to Data Science
- Mathematics for Data Science I
- Introduction to Statistics
- Academic & Professional Communication
- Ethics and Data Privacy
Semester 2 64 credits
Syllabus
- Data Governance & Quality Management
- Computer Programming (Python)
- Database Systems
- Data Wrangling & Visualization
- Mathematics for Data Science II
Semester 3 64 credits
Syllabus
- Probability & Statistical Inference
- Applied Linear Algebra
- Machine Learning Fundamentals
- Big Data Tools & Platforms
- Capstone Project Proposal
Semester 4 64 credits
Syllabus
- Data Mining Techniques
- Natural Language Processing (NLP)
- Capstone Project Execution
- Advanced Statistical Methods
Semester 5 64 credits
Syllabus
- Introduction to Artificial Intelligence
- Web & Cloud Data Technologies
- Project Management for Data Projects
- Advanced Machine Learning
- Domain-Specific Analytics
Semester 6 52 credits
Syllabus
- Industrial Attachment
- Entrepreneurship and Innovation in Data Science (Elective)
- Time Series Analysis and Forecasting (Elective)
- Cloud-Based Machine Learning (Elective)
Eligibility
Entry requirements
- At least 5 O Level / BGCSE subjects with passes in Maths, English, and computer-related subjects with at least 30 points.
- Certificate IV (NCQF Level 4) or equivalent qualification in Information Technology or a related discipline.
- Recognition of Prior Learning (RPL) may be applied in accordance with the institution approved RPL policy and NCQF guidelines.
Career Pathways
Professional opportunities
- Data Scientist
- Data Analyst
- Business Intelligence Analyst
- Machine Learning Specialist
- Data Engineer
- AI Solutions Associate
- Research Analyst
- Analytics Consultant
Admissions Guidance
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