Course Content
KM-01: Introduction to RPA and Digital Transformation
This module introduces learners to the fundamentals of Robotic Process Automation (RPA), digital transformation, and automation technologies used in modern business environments. Learners will explore how businesses use automation to improve efficiency, reduce repetitive tasks, and support digital innovation.
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KM-04: Computing Theory
This module introduces learners to the foundational principles of programming and computing theory used in software development and automation environments. Learners will explore programming languages, programming logic, algorithms, variables, operators, loops, functions, and software applications commonly used in modern computing systems. The module also introduces concepts related to web technologies, databases, artificial intelligence, and software development methodologies.
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KM-05: Data, Databases and Data Scraping
This module introduces learners to the principles of data management, databases, and data scraping used in modern digital and automation environments. Learners will explore how organisations collect, store, analyse, secure, and visualise data to support business processes and decision-making. The module also introduces structured query language (SQL), relational databases, web scraping techniques, and software tools used for analysing and visualising data in automation and RPA environments.
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KM-06: Introduction to RPA for Automation of Processes
This module introduces learners to the foundational concepts, technologies, and processes involved in Robotic Process Automation (RPA). Learners will explore automation principles, business process analysis, workflow automation, process mapping, bots, attended and unattended automation, and the role of RPA in improving operational efficiency. The module also examines how organisations identify processes suitable for automation and how RPA supports digital transformation initiatives.
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KM-07: Robotic Process Automation (RPA)
This module focuses on building an understanding of how to use a toolkit or platform, using a vendor-specific approach, for the creation and deployment of automated processes. Learners will explore variables, arguments, automation selectors, control flow, data manipulation, automation concepts, automation management, and methods used to secure the RPA ecosystem from security risks. The module develops practical knowledge required to build, manage, and support automation solutions within modern RPA environments.
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KM-08: Introduction to RPA Governance, Legislation and Ethics
This module introduces learners to governance, legislation, compliance, ethics, and responsible practices within Robotic Process Automation (RPA) environments. Learners will explore legal requirements, organisational governance, ethical considerations, compliance frameworks, privacy protection, intellectual property, accountability, and professional conduct related to automation technologies. The module also examines how organisations manage risk, maintain compliance, and ensure ethical use of RPA systems within modern digital business environments.
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KM-09: Fundamentals of Design Thinking and Innovation
This module introduces learners to the fundamentals of design thinking and innovation within modern business and technology environments. Learners will explore design thinking principles, human-centered design, creativity, innovation, design concepts, design thinking methodologies, and the practical application of design thinking in software development, cybersecurity, and business problem-solving. The module focuses on developing innovative thinking, problem-solving skills, and creative approaches used in modern workplaces and digital transformation environments.
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KM-10: 4IR and Future Skills
This module focuses on building an understanding of the impact of the Fourth Industrial Revolution (4IR) on communities, individuals, and businesses, as well as the future skills required in modern digital environments. Learners will explore emerging 4IR technologies, computing knowledge, future skills and competencies, business trends, interpersonal and intrapersonal skills, communication methods, workplace teamwork, customer service, and professional workplace practices required within modern organisations and Robotic Process Automation (RPA) environments.
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PM-01: Basic Calculations for Programming
This practical module introduces learners to the mathematical and computational concepts required in programming and automation environments. Learners will develop practical skills in number systems, measurement conversions, mathematical operations, scientific notation, logical calculations, and computational problem solving. The module focuses on applying calculations and numerical reasoning in software development and Robotic Process Automation (RPA) environments. Learners will complete practical activities that strengthen analytical thinking, accuracy, and computational problem-solving skills required in modern digital workplaces.
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PM-02: Basic Programming
This practical module introduces learners to fundamental programming concepts, software toolkits, coding environments, programming paradigms, data types, APIs, functions, logical operations, loops, SQL queries, error handling, and software development processes used in Robotic Process Automation (RPA) environments. Learners will develop practical programming skills by creating coding environments, writing and testing code, working with variables and functions, integrating APIs, handling errors, and developing simple automation solutions using industry-relevant software toolkits and platforms.
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PM-03: Access, Analyse and Visualise Structured Data Using Spreadsheets and Scraping Tools
This practical module focuses on developing the skills required to access, analyse, organise, transform, visualise, and report structured data using spreadsheets, dashboards, pivot tables, databases, and web scraping tools within a Robotic Process Automation (RPA) environment. Learners will work with spreadsheet reporting, dashboards, pivot tables, SQL imports, data models, charts, and web scraping techniques to process and visualise data for business decision-making.
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PM-05: Execute Test Procedures for Evaluating the RPA Solution Performance
This practical module focuses on developing the practical skills required to prepare, execute, evaluate, and improve test procedures for Robotic Process Automation (RPA) solutions. Learners will work with test cases, testing methodologies, simulation tools, workflow evaluations, exception handling, and remedial actions to determine whether an RPA solution passes or fails according to business and technical requirements. Learners will also develop the ability to analyse automation outcomes, identify application and workflow issues, document test evidence, and apply corrective actions to improve automation reliability and performance.
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PM-06: Deploy RPA Solutions Which Emulate Actions of a Human Interacting Within Digital Systems
This practical module focuses on developing the practical skills required to deploy, schedule, monitor, manage, and maintain Robotic Process Automation (RPA) solutions within production environments. Learners will work with unattended and attended robots, deployment procedures, process documentation, auditing dashboards, scheduling systems, and RPA environment management tools. Learners will also develop the ability to schedule automated workflows, deploy bots into production environments, update process documentation, train end-users, monitor runtime activities, and import or export automation solutions between environments.
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PM-07: Modify and Improve Existing RPA Solutions
This practical module focuses on developing the practical skills required to troubleshoot, improve, maintain, and optimise existing Robotic Process Automation (RPA) solutions within operational environments. Learners will work with debugging tools, workflow optimisation techniques, infrastructure changes, software upgrades, regulatory requirements, and process improvement strategies to ensure that automation workflows continue to operate efficiently and reliably. Learners will also develop the ability to investigate alternative solutions, apply continuous improvement techniques, manage changes in technical environments, explore workflow scalability, and update robotic workflows when organisations upgrade RPA software versions.
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PM-08: Function Ethically and Effectively as a Member of a Multidisciplinary Team
This practical module focuses on developing the practical skills required to function ethically, professionally, and collaboratively within multidisciplinary Robotic Process Automation (RPA) environments. Learners will work with business analysts, solution architects, DevOps teams, infrastructure engineers, project managers, business users, and stakeholders throughout the automation life cycle. Learners will also develop the ability to communicate effectively, collaborate across departments, support business process automation initiatives, engage with stakeholders ethically, adapt to organisational policies and infrastructure changes, and contribute to teamwork and business optimisation activities.
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PM-09: Apply Design Thinking Methodologies
This practical module focuses on developing the practical skills required to apply Design Thinking methodologies within problem-solving and innovation environments. Learners will collaborate with multidisciplinary teams to investigate problems, generate innovative ideas, develop prototypes, and test solutions using the Design Thinking process. Learners will also develop the ability to engage in collaborative discussions, participate in innovation workshops, analyse user needs, challenge assumptions, generate creative solutions, and apply the five Design Thinking phases: Empathize, Define, Ideate, Prototype, and Test.
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Occupational Certificate: Robotic Process Automation (RPA) Developer

Lesson Overview

This lesson introduces learners to emerging trends associated with the Fourth Industrial Revolution (4IR). Learners will explore technologies and innovations shaping modern industries and digital environments, including artificial intelligence, cloud computing, cyber security, data science, the Internet of Things (IoT), quality engineering automation, robotic process automation, software programming, design thinking and innovation, and e-waste. The lesson focuses on how these technologies influence businesses, communities, and future workplaces.

Lesson Outcomes

After completing this lesson, learners will be able to:

  • Explain emerging trends associated with 4IR
  • Describe artificial intelligence and its applications
  • Explain cloud computing and its uses
  • Describe cyber security and its importance
  • Explain data science and IoT concepts
  • Describe quality engineering automation and RPA
  • Explain software programming concepts
  • Describe design thinking and innovation
  • Explain e-waste and its impact

KT0101: Artificial Intelligence

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and mimic human actions. AI systems are designed to perform tasks such as learning, reasoning, problem solving, and decision-making.


Characteristics of Artificial Intelligence

AI systems can:

  • Learn from information
  • Adapt to new data
  • Solve problems
  • Make decisions
  • Recognise patterns

The goal of AI is to create systems capable of performing tasks normally requiring human intelligence.


Machine Learning

Machine learning is a subset of AI that allows systems to learn automatically from data without direct human assistance.

Machine learning supports:

  • Data analysis
  • Prediction systems
  • Automation
  • Pattern recognition

Applications of Artificial Intelligence

Examples include:

Application Example
Virtual Assistants Siri, Alexa
Recommendation Systems Netflix, YouTube
Chatbots Customer support
Automation Smart manufacturing

AI is increasingly used across industries to improve efficiency and decision-making.


KT0102: Cloud Computing

Cloud computing refers to the delivery of computing services over the internet (“the cloud”). These services include:

  • Servers
  • Storage
  • Databases
  • Networking
  • Software
  • Analytics

 


Purpose of Cloud Computing

Cloud computing provides:

  • Flexible resources
  • Faster innovation
  • Remote access
  • Cost efficiency

Users can access information and services from remote servers using internet connections.


Examples of Cloud Services

Examples include:

  • Amazon Web Services (AWS)
  • Microsoft Azure
  • Google Cloud

Cloud services support businesses, software development, and digital transformation.


KT0103: Cyber Security

Cyber security involves technologies, processes, and controls used to protect systems, networks, programs, and data from cyber-attacks.


Importance of Cyber Security

Cyber security helps organisations:

  • Protect sensitive data
  • Reduce cyber risks
  • Prevent unauthorised access
  • Protect digital systems

As businesses increasingly depend on digital systems, cyber security becomes more important.


Types of Cyber Security

The learner material identifies five types:

  1. Critical infrastructure security
  2. Application security
  3. Network security
  4. Cloud security
  5. Internet of Things (IoT) security

KT0104: Data Science

Data science focuses on preparing, analysing, and interpreting data to generate useful information and insights.


Functions of Data Science

Data science involves:

  • Data collection
  • Data cleaning
  • Data analysis
  • Pattern recognition
  • Decision support

Organisations use data science to improve decision-making and solve problems.


Applications of Data Science

Examples include:

  • Business analytics
  • Customer behaviour analysis
  • Artificial intelligence
  • Market forecasting

Data science supports modern digital business environments.


KT0105: Internet of Things (IoT)

The Internet of Things (IoT) refers to physical devices connected through the internet that exchange data with other systems and devices.


Examples of IoT Devices

Examples include:

  • Smart home devices
  • Wearable fitness trackers
  • Smart appliances
  • Connected vehicles

IoT improves automation and communication between devices.


KT0106: Quality Engineering Automation

Quality Engineering (QE) automation focuses on improving product and process quality through automated testing and quality management.


Quality Automation

Quality automation uses software tools to perform tests on systems automatically.

Benefits include:

  • Faster testing
  • Reduced human error
  • Improved product quality
  • Increased efficiency

Automation supports software development and operational improvement.


KT0107: Robotic Process Automation

Robotic Process Automation (RPA) is a software technology that uses bots to automate digital tasks and business processes.


Functions of RPA Bots

RPA bots can:

  • Copy and paste information
  • Process emails
  • Perform calculations
  • Extract data
  • Interact with systems
  • Automate workflows

Bots imitate human actions within digital systems.


Benefits of RPA

RPA improves:

  • Productivity
  • Accuracy
  • Efficiency
  • Workflow consistency

RPA is widely used in modern organisations and manufacturing environments.


KT0108: Software Programming

Software programming refers to writing computer code that enables software systems to function.


Types of Programming Languages

The learner material identifies several programming language categories:

  • Procedural programming
  • Functional programming
  • Scripting programming
  • Logic programming
  • Object-oriented programming

Examples of Programming Software

Examples include:

  • Compilers
  • Debuggers
  • Interpreters
  • Assemblers

Programming enables the development of software applications and digital systems.


KT0109: Design Thinking and Innovation

Design thinking is a human-centered approach to innovation that focuses on understanding customer needs and developing creative solutions.


Design Thinking and Innovation

Design thinking supports:

  • Creativity
  • Innovation
  • Problem solving
  • Product development

The approach combines human understanding, research, and practical solution development.


KT0110: e-Waste

E-waste refers to electronic products reaching the end of their useful life.

Examples include:

  • Computers
  • Mobile phones
  • Televisions
  • Printers
  • Electronic appliances

Types of e-Waste

The learner material identifies three categories:

Type Example
Major Appliances Refrigerators, washing machines
Small Appliances Irons, blenders
Computer & Telecommunication Devices Laptops, mobile phones

Importance of Managing e-Waste

Proper e-waste management helps:

  • Reduce environmental pollution
  • Support recycling
  • Protect health and safety
  • Reduce electronic waste accumulation

Responsible disposal and recycling are important in modern digital societies.


Importance of 4IR Emerging Trends

Emerging 4IR technologies continue transforming businesses and societies.

These trends support:

  • Automation
  • Digital transformation
  • Innovation
  • Improved efficiency
  • Global connectivity

Modern organisations increasingly rely on advanced technologies to remain competitive and adaptable.


Key Notes

  • Artificial intelligence simulates human intelligence in machines.
  • Cloud computing provides internet-based computing services.
  • Cyber security protects systems and data from cyber threats.
  • Data science helps organisations analyse and interpret data.
  • IoT connects devices through the internet.
  • Quality engineering automation improves testing efficiency.
  • RPA automates repetitive digital processes.
  • Software programming enables software development.
  • Design thinking supports innovation and problem solving.
  • E-waste includes discarded electronic products.
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