Data Science -NQF Level 4
(SAQA ID: 118708)
Qualification Entry Requirements
- Minimum Entry Requirement: NQF Level 4 with Mathematics
Qualification Rules
In this qualification, 185 total credits are allocated across three module categories:
- Knowledge Modules: 66 credits
- Practical Skill Modules: 59 credits
- Work Experience Modules: 60 credits
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Occupational Certificate: Data Science Practitioner
(SAQA ID: 118708)
This qualification is registered on the Occupational Qualifications Sub-framework (OQSF) at NQF Level 5. It is designed to equip learners with the skills necessary to collect, transform, and analyze data to solve elementary business problems, enabling them to support Data Scientists and broader data-driven initiatives.
1. The purpose of this qualification is to prepare a learner to operate as a Data Science Practitioner. Data Science Practitioners assume custody of data, transforming raw inputs into usable datasets aligned with real-world problems. They apply programming, statistical, and analytical skills to uncover patterns, trends, and insights, ultimately using data visualization and effective communication to guide decision-makers.
A qualified learner will be able to:
- Collect large amounts of structured and unstructured data from primary and secondary sources, then extract and transform these into a usable format.
- Apply data analysis techniques to uncover patterns and trends, solving business-related problems.
- Prepare and present descriptive analytic reports, using programming languages and data visualization tools to explain key findings.
2. Data science is critical to the Fourth Industrial Revolution (4IR), where artificial intelligence, cloud computing, big data, and process automation dominate. Organizations across industries face an urgent shortage of skilled data practitioners who can manage, interpret, and draw meaningful conclusions from ever-growing datasets.
- This qualification addresses the exponential demand for data analysts and data engineers by focusing on the foundational data lifecycle—from collection and wrangling to analysis and reporting.
- It also bridges the gap between entry-level workers and advanced data roles (e.g., Data Scientist), ensuring that learners can handle robust, complex datasets ethically and effectively.
- Graduates will enter the workforce ready to contribute as Data Analyst Assistants, Junior Data Analysts, Data Miners, Data Modellers, or Management Information Analysts, etc.
On completion of this qualification students will be able to:
- Collect and preprocess data from diverse sources, ensuring a clean, usable dataset.
- Analyze datasets, applying statistical methods and logical reasoning to identify trends.
- Visualize and Communicate results effectively through reports, dashboards, or storytelling techniques, guiding data-driven decisions in businesses or community projects.