QCTO Accredited Occupational Qualification:
Data Science Practitioner
Full Qualification | NQF Level 5 | SAQA ID – 118708
Turn your organisation’s raw data into decision-ready insight with our Data Science Practitioner Occupational Qualification.
- Duration: 18 months
- Credits: 185
- Internationally Recognised QCTO Certificate
Understanding the Data Science Practitioner Qualification
Data Science Practitioner Overview
The Data Science Practitioner Occupational Qualification prepares staff to take custody of an organisation’s data and make it usable. Learners collect structured and unstructured data from primary and secondary sources, clean and transform it into robust datasets, apply analysis techniques to uncover patterns and trends, and present descriptive analytic reports that a business can act on.
The toolset is deliberately practical, spanning spreadsheets, statistical techniques, programming and visual analytics platforms, and the qualification closes with a capstone project. The scope is set at practitioner level: your people prepare, analyse and communicate the data that decisions are made on.
What individuals will learn
- Data science and data analysis concepts and where they apply across economic sectors
- Logical thinking and basic calculations applied to data work
- Computers and computing systems, including networks and connectivity
- Computing theory and the principles of programming
- Basic statistics for data analytics
- Statistics essentials applied to data analysis
- Preparing and presenting descriptive analytic reports for decision making
- Data science and data analysis concepts, principles and governance
- Data analysis and visualisation procedures
- Governance, legislation and ethics affecting data work
- Fundamentals of design thinking and innovation
- 4IR technologies and future workplace skills
- Collecting and pre-processing large volumes of structured and unstructured data
- Using visual analytics and business intelligence platforms to analyse and present data
Details to know
Our occupational qualifications are conducted with maximum use of practical application of the skills acquired. After facilitator led training has taken place via the course material, group work and scenario driven exchanges, learners will have the opportunity to relate the information back to their workplace. We offer a fun and relaxed learning environment, where participants will learn from each other, gain knowledge and acquire skills that will result in improved work performance.
This qualification is designed for any individual who is, or wishes to be, part of the information and communication technology (ICT) industry. It will give students the skills they need to excel in this area of your organisation.
Learners will need competency in the following areas:
- NQF Level 4
After completing a Data Science Practitioner Occupational Qualification an individual can work in the following roles:
- Data Science Practitioner
- Data Analyst
- Business Intelligence Analyst
- Reporting Analyst
- Data Visualisation Analyst
- Data Quality Analyst
- Junior Data Engineer
This Occupational Qualification is fully accredited with QCTO. By choosing an Occupational Qualification that is accredited with QCTO, you can ensure you obtain BEE points for your Skills Development efforts and your students receive an education that is of high quality.
Businesses that we have done staff training for:
How to make the most of a Data Science Practitioner QCTO Qualification
Why choose this Qualification?
Analysis and visualisation are the heaviest knowledge modules. KM-08 Data Analysis and Visualisation is worth 16 credits and KM-07 Data Science and Data Analysis a further 12, together almost half the knowledge credits in the qualification. Data that nobody can interpret has no commercial value, and this weighting is aimed squarely at the interpretation step.
The practical modules mirror a real data pipeline. Collecting and pre-processing data, applying analysis techniques to uncover patterns, and preparing descriptive analytic reports are worth 12 credits each, 36 of the 59 practical credits. Learners also work across spreadsheets, statistical tools and visual analytics platforms, so they are not tied to a single vendor’s product.
Sixty workplace credits ending in a capstone project. Work experience runs through data collection and pre-processing, statistical analysis, and visualisation and reporting at 16 credits each, before a 12-credit capstone project using an appropriate toolkit. That final module is the evidence your organisation can point to, because it produces a complete piece of work rather than a set of exercises.
Cybersecurity Analyst NQF Level 5 Module Breakdown
Qualification Breakdown
KM-01 Introduction to Data Science and Data Analysis (Credits 6)
KM-02 Logical Thinking and Basic Calculations: Refresher (Credits 4)
KM-03 Computers and Computing Systems (Credits 4)
KM-04 Computing Theory (Credits 2)
KM-05 Basic Statistics for Data Analytics (Credits 10)
KM-06 Statistics Essentials for Data Analytics (Credits 4)
KM-07 Data Science and Data Analysis (Credits 12)
KM-08 Data Analysis and Visualisation (Credits 16)
KM-09 Introduction to Governance, Legislation and Ethics (Credits 3)
KM-10 Fundamentals of Design Thinking and Innovation (Credits 4)
KM-11 4IR and Future Skills (Credits 1)
PM-01 Apply Code to use a Software Toolkit/Platform in the Field of Study or Employment (Credits 4)
PM-02 Use Spreadsheets to Analyse and Visualise Data (Credits 3)
PM-03 Use a Visual Analytics Platform to Analyse and Visualise Data (Credits 4)
PM-04 Apply Logical Thinking and Maths Refresher (Credits 3)
PM-05 Apply Statistical Tools and Techniques (Credits 4)
PM-06 Collect and Pre-Process Large Amounts of Structured and Unstructured Data (Credits 12)
PM-07 Apply Data Analysis Techniques to Uncover Patterns and Trends in Datasets (Credits 12)
PM-08 Prepare and Present Descriptive Analytic Reports for Decision Making (Credits 12)
PM-09 Participate in a Design Thinking for Innovation Workshop (Credits 3)
PM-10 Collaborate Ethically and Effectively in the Workplace (Credits 2)
WM-01 Data Collection and Pre-processing Processes (Credits 16)
WM-02 Statistical Data Analysis Processes (Credits 16)
WM-03 Data Visualisation and Reporting Processes (Credits 16)
WM-04 Capstone Project using an Appropriate Toolkit (Credits 12)
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