Data Science Practitioner – NQF Level 5
QCTO Learnership
Course overview
The Occupational Certificate: Data Science Practitioner equips learners with the skills to collect, transform, analyse, and present data to solve business problems. Data Science Practitioners bridge the gap between raw data and decision-making by preparing robust datasets, applying statistical and analytical techniques, and producing insightful visual reports. This qualification responds to the increasing demand for skilled data professionals in the era of the Fourth Industrial Revolution (4IR), where organisations rely on big data and analytics to enhance operations and drive innovation.
Learning Outcomes
On completion of this programme, learners will be able to:
A Qualified Learner Will Be Able To:
Entry Requirements
- Minimum Requirement: A National Senior Certificate or equivalent at NQF Level 4 with Mathematics.
- Recognition of Prior Learning (RPL) may provide access for learners with relevant experience or competencies.
International Comparability
This qualification compares favourably with leading global programmes:
- Canada: Similar in scope and duration to the Toronto School of Management’s Diploma in Data Analytics Co-op, including knowledge, practical components, and a capstone project.
- India: Comparable to AnalytixLabs’ “Data Science with Python” course, covering similar competencies in data analysis, statistics, and visualisation. Unlike some international courses that are vendor-specific (e.g., AWS, IBM), this qualification is vendor-agnostic and includes work experience and ethics modules, providing a broader professional foundation.
Occupational Trainer – NQF Level 5 Certification
This programme is registered as an Occupational Certificate (Data Science Practitioner), NQF Level 5 under the Occupational Qualifications Sub-Framework (OQSF). Successful completion prepares learners for external integrated summative assessment through the QCTO.
Potential Career Opportunities
Graduates of this programme may pursue entry-level and junior roles in data science and analytics, including:
Learning Options
Learning may be offered through:
- Classroom-based or blended training with formative assessments.
- Workplace learning and mentorship for practical application.
- Capstone project integrating knowledge, practical skills, and work experience
- External integrated summative assessment conducted by a QCTO-approved Assessment Quality Partner.
Course Details:
