Data Science Practitioner

The purpose of this qualification is to prepare a learner to operate as a Data Science Practitioner. Data Science Practitioners take custody of data and make the data available in a structured form for the Data Scientist to use. They support the data life cycle by collecting, transforming, and analysing data and communicating results to solve elementary business problems. They transform data into robust, comprehensive data sets, aligned with the problem identified in the statement of work and ready for storage. A qualified learner will be able to: • Collect large amounts of structured and unstructured data from primary and secondary sources and extract and transform them into a usable format. • Apply data analysis techniques to uncover patterns and trends in datasets (resultant sets of data that can be viewed as tables or as a "spreadsheet of data") to solve business-related problems. • Prepare and present descriptive analytic reports on patterns and trends using computer programming languages and explain those patterns and trends through e.g., visualisation, storytelling, etc., using data visualization tools.

Recognition of Prior Learning (RPL): RPL for Access to the External Integrated Summative Assessment Accredited providers and approved workplaces must apply the internal assessment criteria specified in the related curriculum document to establish and confirm prior learning. Accredited providers and workplaces must confirm prior learning by issuing a statement of result. RPL for Access to the Qualification • Learners will gain access to the qualification through RPL for Access as provided for in the QCTO RPL Policy. RPL for access is conducted by accredited education institution, skills development provider or workplace accredited to offer that specific qualification/part qualification. • Learners who have acquired competencies of the modules of a qualification or part qualification will be credited for modules through RPL. RPL for access to the external integrated summative assessment Accredited providers and approved workplaces must apply the internal assessment criteria specified in the related curriculum document to establish and confirm prior learning. Accredited providers and workplaces must confirm prior learning by issuing a statement of result. Entry Requirements: The minimum entry requirement for this qualification is: • NQF Level 4 with Mathematics.

The Presidential Commission on 4IR (PC4IR) report states that the key drivers of change in Human Capital and the Future of Work will be ubiquitous high-speed mobile internet, artificial intelligence, widespread adoption of big data analytics and cloud technology. Thus, with the emergence of the '4IR' and the need to properly manage 'Big Data', a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data by enabling high velocity capture, discovery, or analysis, will emerge. The 4th Industrial Revolution (4IR) is a fusion of advances in artificial intelligence (AI), robotics, process automation, the Internet of Things (IoT), genetic engineering, quantum computing, cyber security, cloud computing and data science. There is an exponential demand for data analysts, data engineers, data architects and Data Science Practitioners, in response to the proliferation of complex and voluminous data generated by cloud-businesses and social media networks. To meet this demand, many organisations have started to consider developing skills internally by sharing resources, undertaking training programmes and partnering with others in the industry. This plays a crucial role in establishing a data-driven culture and currently available advanced technology to manipulate these big data and complex datasets. The demand for qualified big data analysts is exceeding supply to the point where it can take many months to fill vacancies. The root problem of this is that big data analytics is a new field and the existing workforce skill sets must be adjusted to be able to work with large, sophisticated datasets. This shortage is acute and is growing exponentially. Recent research indicated that in 2020 the shortage of data scientists can best be summarised as follows: • Year-on-year there is a growth of 37% in job listings for data scientists. • Data scientist ranked 3rd amongst top jobs for 2020. • The average annual salary increase of data scientists is 14%. Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structured and unstructured data. Data science is related to data mining, machine learning and big data. The data science practitioner's duties can include developing strategies for analysing data, preparing data for analysis, exploring, analysing, and visualizing data, building models with data using programming languages and deploying models into applications. This qualification covers the collection and transformation of data, solving business-related problems through the analysis of data to uncover patterns and trends and the preparation and presentation of descriptive analytic reports using programming techniques, mathematics, and statistics. The above information confirms the growing need for the Occupational Certificate: Data Science Practitioner. There is a plethora of similar qualifications registered on the NQF. None of these qualifications are at NQF 5 and they are not occupational qualifications. Data science will bring many benefits to society, touching a wide range of aspects in the daily life of the individual. Scientists can now develop algorithms that can help predict infections based on data analysis, hours before physical symptoms appear. Big data is key to the success of healthcare organizations. They can deliver immunizations, healthcare, and water to some of the world's poorest populations by analysing big data. Companies use the data they collect from the individual to determine what kind of product - whether music, movies, or consumables - to produce. The target group for this qualification is school leavers, graduates from TVET colleges, new entrants into the sector and existing employees who have experience in this field, but without formal recognition of skills and competencies. No professional registration or licencing is expected for Data Science Practitioner to seek employment in the sector. Data Science Practitioners can find employment as Data Analyst Assistants, Junior Data Analysts, Data Miners, Data Modellers, Data Custodians or Management Information Analysts.

Recognition of Prior Learning (RPL): RPL for Access to the External Integrated Summative Assessment Accredited providers and approved workplaces must apply the internal assessment criteria specified in the related curriculum document to establish and confirm prior learning. Accredited providers and workplaces must confirm prior learning by issuing a statement of result. RPL for Access to the Qualification • Learners will gain access to the qualification through RPL for Access as provided for in the QCTO RPL Policy. RPL for access is conducted by accredited education institution, skills development provider or workplace accredited to offer that specific qualification/part qualification. • Learners who have acquired competencies of the modules of a qualification or part qualification will be credited for modules through RPL. RPL for access to the external integrated summative assessment Accredited providers and approved workplaces must apply the internal assessment criteria specified in the related curriculum document to establish and confirm prior learning. Accredited providers and workplaces must confirm prior learning by issuing a statement of result. Entry Requirements: The minimum entry requirement for this qualification is: • NQF Level 4 with Mathematics.

• Collect large amounts of structured and unstructured data from primary and secondary sources and extract and transform them into a usable format.
• Apply data analysis techniques to uncover patterns and trends in datasets (resultant sets of data that can be viewed as tables or as a "spreadsheet of data") to solve business-related problems.
• Prepare and present descriptive analytics reports on patterns and trends using computer programming languages and explain those patterns and trends through e.g., visualization and storytelling etc., using data visualisation tools.