The Data Science Practitioner course at Wheelset Academy prepares learners for practical work in data collection, data analysis, data visualisation, and reporting. It gives you the knowledge, practical skills, and work experience needed to solve real business problems with data.
If you are looking for a Data Science Practitioner course, a trusted Data Science Practitioner training centre, or guidance on Data Science Practitioner apprenticeship requirements, this programme offers a strong path into the world of data. Moreover, it is designed for learners who want to build real skills in data preparation, analysis, reporting, and problem-solving for modern workplaces. The curriculum is aligned to the Occupational Certificate: Data Science Practitioner and lists MICT SETA as the Development Quality Partner.
The Data Science Practitioner course gives learners a practical route into one of the fastest-growing career fields. First, it builds strong foundations in data science concepts, computing systems, programming, and statistics. Then, it moves into applied data collection, data pre-processing, analysis, visualisation, reporting, and workplace-based experience.
At Wheelset Academy, learners can benefit from a structured programme that reflects the official curriculum design. As a result, they do not only learn theory. They also gain practical skills in spreadsheets, coding, analytics platforms, statistical tools, and reporting for decision-making. Furthermore, the programme covers governance, legislation, ethics, design thinking, and 4IR skills, which makes it highly relevant for today’s data-driven workplaces.
This programme is ideal for school leavers with the right entry level, working professionals who want to strengthen their digital skills, and employers who want staff trained in real data workflows. Therefore, whether you want to start a new career or grow in your current role, this Data Science Practitioner apprenticeship-style learning path can help you move forward with confidence.
After completing this programme, learners should be able to:
Collect large amounts of structured and unstructured data from primary and secondary sources.
Clean, transform, and pre-process data into a usable format.
Apply data analysis techniques to uncover patterns and trends in datasets.
Use spreadsheets, code, and visual analytics platforms to analyse and visualise data.
Apply statistical tools and techniques in practical business contexts.
Prepare and present descriptive analytic reports for decision-making.
Work with governance, legislation, ethics, and data protection principles.
Participate effectively in innovation and design thinking processes.
Collaborate ethically and professionally in the workplace.
Complete work experience activities and a capstone project using an appropriate toolkit.
View the full Learning and Development course price, including
application, registration, POE, and flexible payment options.
Yes, this programme is based on the official Counterbalanced Lift Truck F1 Driver Skills Programme curriculum.The programme is set at NQF Level 3, and the quality partner listed in the curriculum is the Transport Education Training Authority (TETA).
To qualify for the Occupational Health and Safety Practitioner Course, learners should meet the following requirements:
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On completion, learners receive a Letter of Competence from Wheelset Academy and, once verified, a Certificate of Competence with a Statement of Results from the QCTO.
Full course fee or range Payment plan options Special discounts
The course is taught in English through interactive methods such as lectures, discussions, case studies, videos, and assessments. Study options include classroom, blended online, or on-site training.
After completing this programme, learners may pursue opportunities such as:
The occupational purpose of the qualification is to support the data life cycle by collecting, transforming, analysing, and communicating data to solve elementary business problems.
This module strengthens problem-solving and mathematical thinking for data work. Therefore, learners revise core calculations, binary concepts, coordinates, operator rules, modulus, increments, and other maths ideas commonly used in analytics and coding.
This module builds understanding of computers, operating systems, networking, cloud computing, security, troubleshooting, and digital tools. As a result, learners gain the technical awareness needed to work confidently in data and IT environments.
This module introduces programming logic, coding principles, algorithms, variables, functions, and software applications. It also exposes learners to tools such as SQL, Python, web technologies, and AI concepts that support data-related work.
This module covers the foundations of statistics used in data analysis. Learners work with concepts such as mean, standard deviation, regression, and sample size determination to build practical understanding of numerical analysis.
This module extends statistical knowledge into descriptive statistics, measures, distributions, confidence intervals, hypothesis testing, regression, classification, clustering, categorical data, and association. Consequently, learners build deeper analytical ability for real datasets.
This module focuses on data science principles, analysis approaches, data quality, governance, and legislation. In particular, it helps learners understand descriptive, diagnostic, predictive, and prescriptive analysis, as well as legal and ethical responsibilities such as data protection.
This module develops stronger skills in analysing data and turning findings into meaningful visual outputs. It supports learners in presenting trends, patterns, and insights in ways that improve business understanding and decision-making.
This module explains responsible data practice. Therefore, learners study governance, legislative compliance, ethical behaviour, access control, and data protection responsibilities that apply in modern organisations.
This module introduces innovation thinking, user-centred problem-solving, and design thinking tools. As a result, learners become better prepared to solve business problems creatively and contribute to improvement initiatives.
This module explores fourth industrial revolution trends and future workplace skills. It helps learners understand how technology is changing business, employment, and the role of data in modern industries.
This practical module helps learners apply core logic and mathematical methods to problem-solving tasks. It builds confidence in working through calculations, rules, and reasoning steps in a practical data context.
This module develops basic coding ability for workplace tools and platforms. In addition, it helps learners use code to support data tasks in practical business or technical settings.
This module trains learners to use spreadsheets for data analysis and visualisation. Therefore, learners practise organising data, performing calculations, and creating clear visual summaries for reporting.
This module introduces learners to visual analytics platforms used to explore data and present findings. As a result, they gain practical experience in turning raw data into dashboards, charts, and actionable views.
This module gives learners practice in using statistical methods and tools on real problems. It strengthens the ability to interpret data correctly and apply suitable analytical approaches in business contexts.
This module develops real data preparation skills. Learners work with structured and unstructured data, programming languages, and tools such as Python, SQL, Power BI, and related technologies to prepare data for analysis.
This module focuses on conducting preliminary analysis, building models, and applying statistical methods to identify useful patterns and trends. Consequently, learners gain the practical analysis skills expected in entry-level data roles.
This module teaches learners how to turn analysed data into clear reports for decision-making. It develops communication, presentation, and storytelling skills so that insights can be shared effectively with stakeholders.
This module gives learners practical exposure to innovation workshops. It helps them apply design thinking methods to workplace challenges while improving teamwork, creativity, and solution development.
This module strengthens workplace behaviour, presentation skills, research, teamwork, participation in meetings, and ethical conduct. Therefore, it supports learners in becoming reliable and professional contributors in a data-driven team.
This work experience module gives learners practical exposure to collecting, handling, and preparing data in workplace settings. It supports real understanding of how raw data becomes usable business information.
This module exposes learners to real analysis processes in the workplace. As a result, they build confidence in applying statistical reasoning and analytical techniques to business data.
This module develops workplace competence in presenting findings through charts, reports, and business communication. It helps learners translate data into useful insights for decision-makers.
This module allows learners to complete an integrated project using a suitable toolkit. Consequently, they demonstrate their ability to apply data collection, analysis, visualisation, and reporting skills in one coherent task.
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A Data Science Practitioner course is a structured programme that teaches learners how to collect, clean, analyse, and present data to solve business problems. This curriculum also includes statistics, computing, coding, ethics, visualisation, and workplace experience.
You can study this programme at a registered provider such as Wheelset Academy, subject to the academy offering the qualification intake and support services for learners.
The curriculum lists NQF 4 as the entry requirement. Wheelset Academy may also have its own registration or support requirements, so it is best to confirm directly before applying.
You will learn data science foundations, programming basics, statistics, data analysis, visualisation, governance, legislation, ethics, design thinking, workplace collaboration, and practical reporting.
Yes. The curriculum contains practical skills modules and work experience modules. Therefore, learners do not only study theory. They also apply tools, platforms, coding, analysis, and reporting in practical settings.
This course can support entry-level roles in data analysis, data reporting, data support, data quality, and analytics assistance. It is especially useful for learners who want to start building a career in data-focused work.
Yes. The curriculum refers to software applications and practical work involving technologies such as Python and SQL, along with other tools and platforms used in data science work.
Yes. Since the entry requirement is NQF 4, this programme can suit beginners who are ready to learn computing, statistics, and data analysis step by step.
A Data Science Practitioner course focuses on practical data handling, data preparation, analysis, visualisation, and reporting for business use. In contrast, a Data Scientist course often goes deeper into advanced modelling, machine learning, and higher-level predictive work. Therefore, the Data Science Practitioner course is a strong starting point for learners who want job-ready, practical skills first.
