Occupational Certificate: Data Science Practitioner SAQA ID: 118708 NQF Level 5

Qualification Details:

Registration start date:           03-02-2022

Registration end date:            03-02-2027

Last date for enrolment:         03-02-2028

Last date for achievement:     03-02-2031

Entry LEVEL Requirements:

The minimum entry requirement for this qualification is: NQF Level 4 with Mathematics. 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.

Associated Occupation:

251102: Data Science Practitioner

Occupation or Specialisation Addressed by this Curriculum

251102001: Data Science Practitioner

PURPOSE AND RATIONALE OF THE QUALIFICATION

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.

Rationale:

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.

Qualification Outcomes:

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.

Skills Development Provider Accreditation Requirements:

The requirements below have been specified on the curriculum and SAQA qualification documents:

Knowledge & Practical Modules

Physical Requirements:

1. The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. Additional QCTO/ MICT SETA requirements may be specified.

Masterskill provides access to the following in the learning programme: Student & Instructor Programme Guidelines; Student & Instructor Learning Materials [eBooks and/or videos (where applicable)]; Additional Resources [website links and/or articles (where applicable)]; Assessment Guidelines; Portfolio of Evidence (including formative, summative assessments); Assessment Guide for Assessors; Programme Strategy (alignment matrix documentation).

2. Valid licenses software and application, including OS.
3. Internet connection and hardware availability.

Providers are required to make own arrangements regarding points 2 & 3 above.

4. Examples and information specified in the scope statement and all the case studies, scenarios and access to hardware and software implied in the scope statements of the modules.
5. Remote learners: Provider must provide business IT simulation system (e.g. invoice processing).

Masterskill provides access to practical assessments and exercises within the student learning material (where applicable).

QCTO/ MICT SETA requirements

Human Resource Requirements:

1. For Knowledge Modules: Lecturer/learner ratio of 1:20 (Maximum), for Practical Modules: Lecturer/learner ratio of 1:10 (Maximum)
2. Qualification of lecturer (SME):
   • Depending on the module an NQF 6 in industry recognised qualification with 1 years’ experience in the IT industry is required.
   • AI vendor certification (where applicable)
   • RPA vendor certification

Masterskill provides free access for instructors to the AI Fluency learning pathway. Additional AI learning programs are also available for instructors to complete.

3. Assessors and moderators: accredited by the MICT SETA

Providers are required to appoint own instructors, assessors and moderators.

Legal Requirements:

1. Legal (product) licenses to use the software for learning and training.

Providers are required to finalise licensing requirements. Refer to the Hardware and Software Requirements stated below.

2. OHS compliance certificate.
3. Ethical clearance (where necessary).

Providers are required to ensure that all legal compliance requirements are met.

Workplace Modules:

Physical Requirements:

1. Tools, equipment, systems, e.g.: company systems, documents, data, relevant meetings, teams and supervisors, design studio, etc.
2. Key processes, e.g.: RPA design, testing and deployment processes project on the go or completed.

Human Resource Requirements:

1. Maximum mentor/learner ratio of 1:3 in the ideal situation.
2. Supervisor/mentor: 2 years’ software development experience

Legal Requirements:

3. Legal (product) licences to use the software for learning and training
4. OHS compliance certificate
5. Ethical clearance (where necessary)

Providers are required to make all necessary arrangements regarding the workplace module components.

Hardware and Software requirements

Masterskill provides practical assessments and exercises within the student learning material. Practical modules which require hardware and software requirements are listed below. Additional information on the hardware and software requirement is available in each of the learning material. ·                LU5: Computing Systems o      Each student and instructor will need to have a computer running Windows 10 and Microsoft Hyper-V. o      Internet access, used to access websites, download software, and use cloud storage. o      A free Microsoft account for each student. o      Tools needed for assembling and troubleshooting computers o      Desktop or laptop computer for assembly and disassembly tasks o      Any assortment of internal hardware components (CPU, motherboard, drives, RAM, PSU, cooler, cards, etc.) ·                LU6a: Introduction to Programming o      Each student and instructor will need to have a computer running Microsoft Visual Studio Community 2022 ·                LU6c: Data Analysis and Visualization o      Each student and instructor will need to have a computer running §   Microsoft® Office Professional Plus 2019 or Office 365™ §   Microsoft® Windows® 10 Professional or Enterprise §   Microsoft® SQL Server® §   Microsoft® SQL Server® Management Studio (SSMS)

 

Work Opportunities

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.

Data Science Practitioners can find employment as Data Analyst Assistants, Junior Data Analysts, Data Miners, Data Modellers, Data Custodians or Management Information Analysts. 

Further Learning

After completion of this occupational certificate learners may opt to articulate vertically to:

• National Certificate: Business Analysis, NQF Level 6.

Horizontal articulation may also be possible and learners could enroll for:

• National Certificate: Business Analysis Support Practice, NQF Level 5.

This South African qualification compares favourably with the competencies covered in the international qualifications and programmes. Reference to international comparability is made in the qualification document which may be obtained from the SAQA website.

Masterskill provides access to CertNexus international exams.

Learners may enroll for international exam certifications such as:

• CertNexus: DSBIZ™ (Exam DSZ-210)
• CertNexus: Certified Data Science Practitioner (CDSP) (Exam DSP-210)

Curriculum Information

Learning Unit 1a: Essential Business Skills (3 CREDITS)

59 hrs / 7 days

• No associated ELO
• 251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits
• 251102001-PM-10, Collaborate Ethically and Effectively in the Workplace, Level 5, 2 Credits

251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits

The main focus of the learning in this knowledge module is to build an understanding of the impact of 4IR on communities, individuals and businesses and important skills for future needs

• KM-11-KT03: Future skills and competencies (4IR) (10%)
• KM-11-KT05: Interpersonal skills (5%)
• KM-11-KT06: Intrapersonal skills (5%)
• KM-11-KT07: Communication principles and methods (5%)
• KM-11-KT08: Written business communication (7%)
• KM-11-KT09: Presentation skills (7%)
• KM-11-KT10: Teamwork in the workplace (10%)
• KM-11-KT11: Committees and meetings (5%)

251102001-PM-10, Collaborate Ethically and Effectively in the Workplace, Level 5,
2 Credits

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to function ethically and effectively in the workplace

• PM-10-PS01: Present information to an audience
• PM-10-PS04: Collaborate with team members in the workplace
• PM-10-PS05: Attend and participate in meetings

Content: Above knowledge and practical module requirements will be covered by eBook and additional resources via Masterskill LMS platform.

Suggested Contact Hrs: 22 hrs

Assessments: 6x Formative Assessments and 2x Summative Assessment is available via Masterskill LMS and ePortfolio platforms.

Suggested Assessment Hrs: 37 hrs

Learning Unit 1B: 4ir emerging trends

7 hrs / 1 day

• No associated ELO
• 251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits (credits calculated in LU1a)
• 251102001-PM-10, Collaborate Ethically and Effectively in the Workplace, Level 5, 2 Credits (credits calculated in LU1a)

251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits

The main focus of the learning in this knowledge module is to build an understanding of the impact of 4IR on communities, individuals and businesses and important skills for future needs

• KM-11-KT01: 4 IR emerging trends (10%)
• KM-11-KT04 : 4 IR trends affecting businesses (10%)

251102001-PM-10, Collaborate Ethically and Effectively in the Workplace, Level 5, 2 Credits

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to function ethically and effectively in the workplace

• PM-10-PS02: Conduct basic research (gather and explore data and information) on 4IR skills and application opportunities in the workplace

Content: Above knowledge and practical module requirements will be covered by additional resources via Masterskill LMS platform.

Suggested Contact Hrs: 6 hrs

Assessments: 1x Formative Assessments and 1x Summative Assessment is available via Masterskill LMS and ePortfolio platforms.

Suggested Assessment Hrs: 1 hr

LEARNING UNIT 2: Understanding the Business Environment & Governance (3 credits)

50 hrs / 6 days

• No associated ELO
• 251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits (credit calculated with LU1a)
• 251102001-PM-10, Collaborate Ethically and Effectively in the Workplace, Level 5, 2 Credits (credit calculated with LU1a)
• 251102001-KM-09, Introduction to Governance, Legislation and Ethics, Level 4, 3 Credits

251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits

The main focus of the learning in this knowledge module is to build an understanding of the impact of 4IR on communities, individuals and businesses and important skills for future needs

• KM-11-KT12: Job descriptions and profiles (5%)
• KM-11-KT13: Customers and stakeholders (7%)
• KM-11-KT14: Customer service (7%)

251102001-PM-10, Collaborate Ethically and Effectively in the Workplace, Level 5,
2 Credits

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to function ethically and effectively in the workplace

• PM-10-PS03: Ensure compliance with the code of conduct and governance in the workplace

251102001-KM-09, Introduction to Governance, Legislation and Ethics, Level 4,
3 Credits

The main focus of the learning in this knowledge module is to acquire general knowledge and understanding of the various legislations governing the workplace and their implications for the employer and employees. The learning of this module will also enable the learner to acquire an understanding of the principles of areas of performance management, business planning concepts, costing of products and concepts of general ethical behaviour and its impact in the workplace

• KM-09-KT01: Governance (20%)
• KM-09-KT02: Legislation governing workplaces (15%)
• KM-09-KT03: Introduction to ethics and security (5%)
• KM-09-KT04: Ethics at work (14%)
• KM-09-KT05: Security (15%)
• KM-09-KT06: Performance management (10%)
• KM-09-KT07: Business planning (7%)
• KM-09-KT08: Costing of products (7%)
• KM-09-KT09: Resources (7%)

Content: Above knowledge and practical module requirements will be covered by online by eBook and additional resources via Masterskill LMS platform.  

Suggested Contact Hrs: 24 hrs

Assessments: 4x Formative Assessments and 3x Summative Assessments are available via Masterskill LMS and ePortfolio platforms.

Suggested Assessment Hrs: 26 hrs

LEARNING UNIT 3: Design Thinking and Innovation (Data Science Practitioners) (7 credits)

42 hrs  /  5 days

• No Associated Exit Level Outcome
• 251102001-KM-10, Fundamentals of Design Thinking and Innovation, NQF Level 4, Credits 4
• 251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits (credit calculated with LU1a)
• 251102001-PM-09, Participate in a Design Thinking for Innovation Workshop, NQF Level 5, Credits 3

251102001-KM-10, Fundamentals of Design Thinking and Innovation, NQF Level 4, Credits 4

The main focus of the learning in this knowledge module is to build an understanding of design thinking principles and application in the workplace

• KM-10-KT01: Introduction to design thinking (15%)
• KM-10-KT02: The human element (10%)
• KM-10-KT03: Creativity (20%)
• KM-10-KT04: Innovation (20%)
• KM-10-KT05: Design (10%)
• KM-10-KT06: Design thinking methodology (10%)
• KM-10-KT07: Application of design thinking (15%)

251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits

The main focus of the learning in this knowledge module is to build an understanding of the impact of 4IR on communities, individuals and businesses and important skills for future needs

• KM-11-KT03: Future skills and competencies (4IR) (10%)

251102001-PM-09, Participate in a Design Thinking for Innovation Workshop, NQF Level 5, Credits 3

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to participate in a design thinking intervention, apply design thinking methodologies and look for opportunities to apply the same methodology in world-of-work and personal life

• PM-09-PS01: Collaborate with team members to apply innovative and problem solving strategies.
• PM-09-PS02: Apply design thinking process to solve a problem creatively and innovatively

Content: Above knowledge and practical module requirements will be covered by online by eBooks and additional resources via Masterskill LMS platform.

Suggested Contact Hrs: 16 hrs

Assessments: 5 x Formative Assessments and 2 x Summative Assessment are available via Masterskill LMS and ePortfolio platforms.

Suggested Assessment Hrs: 26 hrs

LEARNING UNIT 4: logical thinking and basic calculations (21 credits)

36 hrs / 4 ½ days

• No Associated Exit Level Outcome
• 251102001-KM-02, Logical Thinking and Basic Calculations: Refresher, NQF Level 4, Credits 4
• 251102001-KM-05, Basic Statistics for Data Analytics, NQF Level 4, Credits 10
• 251102001-KM-06, Statistics Essentials for Data Analytics, NQF Level 5, Credits 4
• 251102001-PM-01, Apply Logical Thinking and Maths Refresher, NQF Level 4, Credits 3
• 251102001-PM-07, Apply Data Analysis Techniques to Uncover Patterns and Trends in Datasets, NQF Level 5, Credits 12 (credits calculated with LU6c)

251102001-KM-02, Logical Thinking and Basic Calculations: Refresher, NQF Level 4, Credits 4

The main focus of the learning in this knowledge module is to acquire mathematical thinking theory for solving problems and acquire basic maths knowledge for use during data analytics. The learning will enable learners to demonstrate an understanding of:

• KM-02-KT01: Mathematical thinking skills for problem solving 15%
• KM-02-KT02: Conversion between decimal and binary systems 5%
• KM-02-KT03: Expressing size and magnitude 5%
• KM-02-KT04: Error in calculations 10%
• KM-02-KT05: Cartesian coordinate system 10%
• KM-02-KT06 : Pythagorean theorem for finding the distance between two points 5%
• KM-02-KT07: Operator precedence 10%
• KM-02-KT08: Integer division 10%
• KM-02-KT09 : Modulus 10%
• KM-02-KT10 : Increments 10%
• KM-02-KT11: Mixing types 10%

251102001-KM-05, Basic Statistics for Data Analytics, NQF Level 4, Credits 10

The main focus of the learning in this knowledge module is to build an understanding of basic statistics as it pertains to and is applied in data analysis. The learning will enable learners to demonstrate an understanding of:

• KM-05-KT01: Mean 25%
• KM-05-KT02: Standard deviation 25%
• KM-05-KT03: Regression 25%
• KM-05-KT04: Sample size determination 25%

251102001-KM-06, Statistics Essentials for Data Analytics, NQF Level 5, Credits 4

The main focus of the learning in this knowledge module is to build an understanding of statistics essentials as they pertain to and are applied in data analysis. The learning will enable learners to demonstrate an understanding of:

• KM-06-KT01: Sample or population data 2%
• KM-06-KT02: Fundamentals of descriptive statistics 7%
• KM-06-KT03: Measures 7%
• KM-06-KT04: Distributions 7%
• KM-06-KT05: Estimators and Estimates 7%
• KM-06-KT06: Confidence intervals advanced topics 3%
• KM-06-KT07: Hypothesis testing 15%
• KM-06-KT08: Fundamentals of regression analysis 13%
• KM-06-KT09: Subtleties of regression analysis 13%
• KM-06-KT10: Categorical data 5%
• KM-06-KT11: Classification 10%
• KM-06-KT12: Clustering 4%
• KM-06-KT13: Association 7%

251102001-PM-01, Apply Logical Thinking and Maths Refresher, NQF Level 4, Credits 3

The focus of the learning in this module is on providing the learner with an opportunity to acquire mathematical thinking skills for solving problems and to acquire basic maths skills for using software toolkits or platforms. Functional understanding of maths and for logical reasoning. The learner will be required to:

• PM-01-PS01: Number bases and measurement units
• PM-01-PS02: Basic math
• PM-01-PS03: Operator precedence
• PM-01-PS04: Integer division
• PM-01-PS05: Functions, limits and continuity
• PM-01-PS06: Differential calculus of single variable functions
• PM-01-PS07: Modulus
• PM-01-PS08: Increments
• PM-01-PS09: Mixing types
• PM-01-PS10: Casting (timing and contextualising)

251102001-PM-07, Apply Data Analysis Techniques to Uncover Patterns and Trends in Datasets, NQF Level 5, Credits 12 (credits calculated with LU6c)

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to conduct preliminary analysis of data. The learner will be required to:

• PM-07-PS01: Apply the steps in the process for data analysis
• PM-07-PS03: Select and apply statistical principles, methods, techniques and tools to analyse data

Content: Above knowledge and practical module requirements will be covered by online by eBook and additional resources via Masterskill LMS platform.

Suggested Contact Hrs: 25 hrs

Assessments: 11 x Formative Assessments and 1 x Summative Assessments are available via Masterskill LMS and ePortfolio platforms.

Suggested Assessment Hrs: 11 hrs

LEARNING UNIT 5: COMPUTing SYSTEMS (4 CREDITS)

43 hrs / 5 days

• No Associated Exit Level Outcome
• 251102001-KM-03, Computers and Computing Systems, NQF Level 4, Credits 4

251102001-KM-03, Computers and Computing Systems, NQF Level 4, Credits 4

The main focus of the learning in this knowledge module is to build an understanding of what computers can do and the processes that make them function in terms of the four major parts: input, output, CPU (central processing unit) and memory. It gives an overview of networks and connectivity as well as security issues pertaining to IT ecosystems. The learning will enable learners to demonstrate an understanding of:

• KM-03-KT01: Problem solving skills for IT Professionals 10%
• KM-03-KT02: Input and output devices 5%
• KM-03-KT03: Installing printers 5%
• KM-03-KT04: Mobile devices, multimedia, and laptop computers 5%
• KM-03-KT05: Preventative maintenance 5%
• KM-03-KT06: Troubleshooting procedures 5%
• KM-03-KT07: Introduction to operating systems 10%
• KM-03-KT08: Managing files 5%
• KM-03-KT09: Applications utility, troubleshooting and optimization 10%
• KM-03-KT10: Introduction to networking and wireless connections 10%
• KM-03-KT11: Introduction to recovery 5%
• KM-03-KT12: Cloud computing 10%
• KM-03-KT13: Security fundamentals 10%
• KM-03-KT14: Programming and development 5%

Content: Above knowledge and practical module requirements will be covered by online by eBooks and additional resources via Masterskill LMS platform.

Suggested Contact Hrs: 30 hrs, 30 min

Assessments: 4 x Formative Assessments and 3 x Summative Assessments are available via Masterskill LMS and ePortfolio platforms.

Suggested Assessment Hrs: 12 hrs 30 min

LEARNING UNIT 6a: INTRODUCTION TO PROGRAMMING (18 CREDITS)

69 hrs / 8 ½ days

• No Associated Exit Level Outcome
• 251102001-KM-04, Computing Theory, NQF Level 4, Credits 2
• 251102001-PM-02, Apply Code to use a Software Toolkit/Platform in the Field of Study or Employment, NQF Level 4, Credits 4
• 251102001-PM-06, Collect and Pre-Process Large Amounts of Structured and Unstructured Data, NQF Level 5, Credits 12
• 251102001-KM-03, Computers and Computing Systems, NQF Level 4, Credits 4 (credits calculated with LU5)
• 251102001-KM-11, 4IR and Future Skills, NQF Level 4, Credits 1 (credits calculated in LU1a)

251102001-KM-04, Computing Theory, NQF Level 4, Credits 2

The main focus of the learning in this knowledge module is to build an understanding of programming as creating a set of instructions to a computer on how to perform a task using coding and programming languages. The learning will enable learners to demonstrate an understanding of:

• KM-04-KT01: Introduction to programming languages 30%
• KM-04-KT02: Programming basics 40%
• KM-04-KT03: Software applications 30%

251102001-PM-02, Apply Code to use a Software Toolkit/Platform in the Field of Study or Employment, NQF Level 4, Credits 4

The focus of the learning in this module is on providing the learner with an opportunity to acquire the ability to apply basic programming skills and code to use a software toolkit/platform in the field of study or employment. The learner will be required to:

• PM-02-PS01: Source and compare at least three software toolkits/platforms/ languages used in your field of studies.
• PM-02-PS02: Identify and contrast four (4) paradigms.
• PM-02-PS03: Create a programming environment (tailored to a specific tool or platform).
• PM-02-PS04: Write code using a programming language for giving instructions for use of a software toolkit/platform.
• PM-02-PS05: Select and use correct data types (tailored to a specific tool or platform).
• PM-02-PS06: Use complex types to organise data (tailored to a specific tool or platform).
• PM-02-PS07: Add API’s (Application Programming Interface) to an application (tailored to a specific tool or platform).
• PM-02-PS08: Define a function (tailored to a specific tool or platform).
• PM-02-PS09: Use logical branch statements and comparison operators (tailored to a specific tool or platform).
• PM-02-PS10: Code loops (tailored to a specific tool or platform).
• PM-02-PS11: Use and apply variable scopes (tailored to a specific tool or platform).
• PM-02-PS12: Create queries to pull desired data using a structured query language (SQL) (applicable to data base) (tailored to a specific tool or platform).
• PM-02-PS13: Handle errors and troubleshooting (tailored to a specific tool or platform).
• PM-02-PS14: Identify the general steps for writing code (tailored to a specific tool or platform).
• PM-02-PS15: Execute practical exercises 1, 2 and 3 using the specified product set.

251102001-PM-06, Collect and Pre-Process Large Amounts of Structured and Unstructured Data, NQF Level 5, Credits 12

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to collect and clean large amounts of structured and unstructured data. The learner will be required to:

• PM-06-PS01: Apply the first steps of the data science life cycle
• PM-06-PS02: Work with programming languages and software packages, e.g. SAS, R, Python, etc.

251102001-KM-03, Computers and Computing Systems, NQF Level 4, Credits 4 (credits calculated with LU5)

The main focus of the learning in this knowledge module is to build an understanding of what computers can do and the processes that make them function in terms of the four major parts: input, output, CPU (central processing unit) and memory. It gives an overview of networks and connectivity as well as security issues pertaining to IT ecosystems. The learning will enable learners to demonstrate an understanding of:

• KM-03-KT01: Problem solving skills for IT Professionals 10%
• KM-03-KT02: Input and output devices 5%
• KM-03-KT03: Installing printers 5%

251102001-KM-11, 4IR and Future Skills, NQF Level 4, Credits 1 (credits calculated in LU1a)

The main focus of the learning in this knowledge module is to build an understanding of the impact of 4IR on communities, individuals and businesses and important skills for future needs. The learning will enable learners to demonstrate an understanding of:

• KM-11-KT01: 4 IR emerging trends 10%
• KM-11-KT02: Computing Knowledge 7%
• KM-11-KT03: Future skills and competencies (4IR) 10%

Content: Above knowledge and practical module requirements will be covered by online by eBook and additional web resources via Masterskill LMS platform.

Suggested Contact Hrs: 49 hrs 30 min

Assessments: 8 x Formative Assessments and 1 x Summative Assessments are available via Masterskill LMS and ePortfolio platforms.

Suggested Assessment Hrs: 20 hrs 10 min

LEARNING UNIT 6b: Data Science (18 credits)

12 hrs 20 min / 1 ½ days

• ELO2: 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.
• ELO3: 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.
• 251102001-KM-01, Introduction to Data Science and Data Analysis, NQF Level 4, Credits 6
• 251102001-KM-07, Data Science and Data Analysis, NQF Level 5, Credits 12

251102001-KM-01, Introduction to Data Science and Data Analysis, NQF Level 4, Credits 6

The main focus of the learning in this knowledge module is to build an understanding of the concepts of data analytics and the application of data science and analysis in the economic sectors. The learning will enable learners to demonstrate an understanding of:

• KM-01-KT01: What is data science? 15%
• KM-01-KT02: Why data science? 10%
• KM-01-KT03: Sources of data 10%
• KM-01-KT04: Ensuring access to accurate data 20%
• KM-01-KT05: Applications of data science 15%
• KM-01-KT06: Attributes of a Data Science Practitioner 20%
• KM-01-KT07: Big Data 10%

251102001-KM-07, Data Science and Data Analysis, NQF Level 5, Credits 12

The main focus of the learning in this knowledge module is to build an understanding of concepts, principles and governance within data analytics. The learning will enable learners to demonstrate an understanding of:

• KM-07-KT01: Data science 15%
• KM-07-KT02 : Approaches in data analysis 25%
• KM-07-KT03 : Data quality 10%
• KM-07-KT04: Best practices for data governance 10%
• KM-07-KT05 : Legislation (e.g. POPI Act) 40%

Content: Above knowledge and practical module requirements will be covered by online by eBooks via Masterskill LMS platform.

Suggested Contact Hrs: 4 hrs

Assessments: 3 x Formative Assessments and 1 x Summative Assessments are available via Masterskill LMS and ePortfolio platforms.

Suggested Assessment Hrs: 8 hrs 20 min

LEARNING UNIT 6c: Data Analysis, (51 credits)

106 hrs 45 min / 13 ½ days

• ELO1: Collect large amounts of structured and unstructured data from primary and secondary sources and extract and transform them into a usable format.
• 251102001-KM-08, Data Analysis and Visualisation, NQF Level 5, Credits 16
• 251102001-PM-03, Use Spreadsheets to Analyse and Visualise Data, NQF Level 4, Credits 3
• 251102001-PM-04, Use a Visual Analytics Platform to Analyse and Visualise Data, NQF Level 5, Credits 4
• 251102001-PM-05, Apply Statistical Tools and Techniques, NQF Level 5, Credits 4
• 251102001-PM-07, Apply Data Analysis Techniques to Uncover Patterns and Trends in Datasets, NQF Level 5, Credits 12
• 251102001-PM-08, Prepare and Present Descriptive Analytic Reports for Decision Making, NQF Level 5, Credits 12
• 251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits (credits calculated with LU1a)
• 251102001-KM-04, Computing Theory, NQF Level 4, Credits 2 (credits calculated with LU6a)
• 251102001-PM-02, Apply Code to use a Software Toolkit/Platform in the Field of Study or Employment, NQF Level 4, Credits 4 (credits calculated with LU6a)
• 251102001-PM-06, Collect and Pre-Process Large Amounts of Structured and Unstructured Data, NQF Level 5, Credits 12 (credits calculated with LU6a)

251102001-KM-08, Data Analysis and Visualisation, NQF Level 5, Credits 16

The main focus of the learning in this knowledge module is to build an understanding of data analysis and visualisation procedures. The learning will enable learners to demonstrate an understanding of:

• KM-08-KT01: Introduction to Business Analytics 5%
• KM-08-KT02: Introduction to business processes, analysis and process modelling 20%
• KM-08-KT03: Introduction to Data Science Programs 5%
• KM-08-KT04: Data Analytics 15%
• KM-08-KT05: Wrangling 10%
• KM-08-KT06: Data Structures 5%
• KM-08-KT07: Data Visualization 5%
• KM-08-KT08: High-throughput 15%
• KM-08-KT09: High-dimensional data analysis 10%
• KM-08-KT10: Basic machine learning and artificial intelligence concepts 10%

251102001-PM-03, Use Spreadsheets to Analyse and Visualise Data, NQF Level 4, Credits 3

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to use spreadsheets to analyse and visualise data. The learner will be required to:

• PM-03-PS01: Report data using spreadsheets
• PM-03-PS02: Summarise and format data using spreadsheet tables
• PM-03-PS03: Create, use and edit pivot tables and pivot charts
• PM-03-PS04: Create, use and edit dashboards
• PM-03-PS05: Create and configure hierarchies and time data
• PM-03-PS06: Apply a spreadsheet data model
• PM-03-PS07: Import data from files
• PM-03-PS08: Import data from databases
• PM-03-PS09: Import data from reports
• PM-03-PS10: Visualize data
• PM-03-PS11: Scrape data from the web using an appropriate tool

251102001-PM-04, Use a Visual Analytics Platform to Analyse and Visualise Data, NQF Level 5, Credits 4

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to use Business Intelligence (BI) Technologies to analyse and visualise data (BI toolsets and technologies refer to e.g. Power BI, R, Python, Tableau, Hadoop, Spark, etc.). The learner will be required to:  

• PM-04-PS01: Use spreadsheet data with BI technologies
• PM-04-PS02: Self-service BI technology solutions
• PM-04-PS03: Shape and combine data
• PM-04-PS04: Model data
• PM-04-PS05: Use interactive data visualizations to represent data graphically
• PM-04-PS06: Access data
• PM-04-PS07: Use visualisation tools to present data as meaningful insights

251102001-PM-05, Apply Statistical Tools and Techniques, NQF Level 5, Credits 4

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to apply various methods and tools to analyse data and create meaningful insights. The learner will be required to:

• PM-05-PS01: Write queries
• PM-05-PS02: Write SELECT queries
• PM-05-PS03: Query multiple tables
• PM-05-PS04: Sort and filter data
• PM-05-PS05: Use SQL server data types
• PM-05-PS06: Use data manipulation language (DML) to modify data
• PM-05-PS07: Use built-in functions
• PM-05-PS08: Group and aggregate data
• PM-05-PS09: Use subqueries
• PM-05-PS10: Use table expressions
• PM-05-PS11: Use set operators
• PM-05-PS12: Use ranking, offset and aggregate functions
• PM-05-PS13: Write queries using pivoting and grouping sets
• PM-05-PS14: Execute stored procedures
• PM-05-PS15: Program with SQL
• PM-05-PS16: Implement error handling
• PM-05-PS17: Implement transactions

251102001-PM-07, Apply Data Analysis Techniques to Uncover Patterns and Trends in Datasets, NQF Level 5, Credits 12

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to conduct preliminary analysis of data. The learner will be required to:

• PM-07-PS02: Design and build a model
• PM-07-PS04: Apply statistical tools and techniques to collect, pre-process and analyse data

251102001-PM-08, Prepare and Present Descriptive Analytic Reports for Decision Making, NQF Level 5, Credits 12

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to explore and model to extract meaningful information and insights and present such insights. The learner will be required to:

• PM-08-PS01: Explore data/visualise the data using a given platform
• PM-08-PS02: Model the data to extract meaningful information and insights
• PM-08-PS03: Communicate results

251102001-KM-11, 4IR and Future Skills, Level 4, 1 Credits (credits calculated with LU1a)

The main focus of the learning in this knowledge module is to build an understanding of the impact of 4IR on communities, individuals and businesses and important skills for future needs. The learning will enable learners to demonstrate an understanding of:

• KM-11-KT02: Computing Knowledge -7%
• KM-11-KT04: 4 IR trends affecting businesses -10%

251102001-KM-04, Computing Theory, NQF Level 4, Credits 2 (credits calculated with LU6a)

The main focus of the learning in this knowledge module is to build an understanding of programming as creating a set of instructions to a computer on how to perform a task using coding and programming languages. The learning will enable learners to demonstrate an understanding of:

• KM-04-KT03: Software applications 30%

251102001-PM-02, Apply Code to use a Software Toolkit/Platform in the Field of Study or Employment, NQF Level 4, Credits 4 (credits calculated with LU6a)

The focus of the learning in this module is on providing the learner with an opportunity to acquire the ability to apply basic programming skills and code to use a software toolkit/platform in the field of study or employment. The learner will be required to:

• PM-02-PS12: Create queries to pull desired data using a structured query language (SQL) (applicable to data base) (tailored to a specific tool or platform)

251102001-PM-06, Collect and Pre-Process Large Amounts of Structured and Unstructured Data, NQF Level 5, Credits 12 (credits calculated with LU6a)

The focus of the learning in this module is on providing the learner with an opportunity to acquire the skills to collect and clean large amounts of structured and unstructured data. The learner will be required to:

• PM-06-PS01: Apply the first steps of the data science life cycle

Content: Above knowledge and practical module requirements will be covered by online by eBooks.

Suggested Contact Hrs: 61 hrs 15 min

Assessments: 6 x Formative Assessments and 3 x Summative Asses