Occupational Certificate

Data Science Practitioner

SAQA ID: 118708 - NQF Level: 5 - Credits: 179 - Duration: 18 Months

Course Overview

The Occupational Certificate: Data Science Practitioner equips learners with skills to collect, preprocess, analyze, and visualize data to uncover patterns and trends, addressing the growing demand for data professionals in the 4th Industrial Revolution (4IR) and digital economy. This qualification prepares learners to solve business-related problems using data analysis techniques and visualization tools.

Course Structure

The qualification consists of Knowledge Modules, Practical Skill Modules, and Work Experience Modules.

Purpose

This qualification enables learners to function as Data Science Practitioners, capable of collecting and transforming large datasets, applying statistical and analytical techniques, and presenting actionable insights through descriptive reports. It fosters problem-solving, data-driven decision-making, and innovation in the ICT sector.

Entry Requirements

Minimum Qualification: NQF Level 4 with Mathematical Literacy and Communication.

Knowledge Modules (66 Credits)

KM-01 Introduction to Data Science and Data Analysis, Level 4, 6 Credits.
KM-02 Logical Thinking and Basic Calculations: Refresher, Level 4, 4 Credits.
KM-03 Computers and Computing Systems, Level 4, 4 Credits.
KM-04 Computing Theory, Level 4, 2 Credits.
KM-05 Basic Statistics for Data Analytics, Level 4, 10 Credits.
KM-06 Statistics Essentials for Data Analytics, Level 5, 4 Credits.
KM-07 Data Science and Data Analysis, Level 5, 12 Credits.
KM-08 Data Analysis and Visualisation, Level 5, 16 Credits.
KM-09 Introduction to Governance, Legislation and Ethics, Level 4, 3 Credits.
KM-10 Fundamentals of Design Thinking and Innovation, Level 4, 4 Credits.
KM-11 4IR and Future Skills, Level 4, 1 Credit.

Practical Skill Modules (59 Credits)

PM-01 Apply Logical Thinking and Maths Refresher, Level 4, 3 Credits.
PM-02 Apply Code to Use a Software Toolkit/Platform in the Field of Study or Employment, Level 4, 4 Credits.
PM-03 Use Spreadsheets to Analyse and Visualise Data, Level 4, 3 Credits.
PM-04 Use a Visual Analytics Platform to Analyse and Visualise Data, Level 5, 4 Credits.
PM-05 Apply Statistical Tools and Techniques, Level 5, 4 Credits.
PM-06 Collect and Pre-Process Large Amounts of Unruly Data, Level 5, 12 Credits.
PM-07 Apply Data Analysis Techniques to Uncover Patterns and Trends in Datasets, Level 5, 12 Credits.
PM-08 Prepare and Present Descriptive Analytic Reports for Decision Making, Level 5, 12 Credits.
PM-09 Participate in a Design Thinking for Innovation Workshop, Level 5, 3 Credits.
PM-10 Collaborate Ethically and Effectively in the Workplace, Level 5, 2 Credits.

Work Experience Modules (60 Credits)

WM-01 Data Collection and Pre-processing Processes, Level 5, 16 Credits.
WM-02 Statistical Data Analysis Processes, Level 5, 16 Credits.
WM-03 Data visualisation and Reporting Processes, level 5, 16 Credits.
WM-04 Capstone Project Using an Appropriate Toolkit, Level 5, 12 Credits.

Learning Outcomes

Upon completion, learners will demonstrate:

Collect large amounts of structured and unstructured data from primary and secondary sources and transform them into a usable format.
Apply data analysis techniques to uncover patterns and trends in datasets to solve business-related problems.
Prepare and present descriptive analysis reports on patterns and trends to support decision-making.

Career Opportunities

Graduates can pursue roles such as: