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.
0/7
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.
0/7
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.
0/13
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.
0/7
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.
0/15
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.
0/19
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.
0/15
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.
0/29
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.
0/16
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.
0/15
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.
0/12
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.
0/4
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.
0/7
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.
0/6
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.
0/7
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.
0/3
Occupational Certificate: Robotic Process Automation (RPA) Developer

Lesson Outcomes

After completing this practical lesson, learners will be able to:

  • Analyse structured business data
  • Identify suitable variable types
  • Create and use arrays in automation workflows
  • Create and use dictionaries for data storage
  • Organise and process automation data efficiently
  • Test and validate processed data

Overview

Robotic Process Automation (RPA) solutions rely on structured data handling to process information accurately and efficiently. Variables, arrays, and dictionaries are commonly used within automation workflows to store, retrieve, organise, and manipulate business data.

This practical lesson introduces learners to data analysis techniques, variable identification, arrays, dictionaries, and structured data processing within RPA environments. Learners will complete practical activities involving the analysis and handling of business process information within automation workflows.


Scenario: Automated Customer Information Processing

A customer service department uses an RPA solution to process customer registration information, product selections, support requests, and account updates.

The software robot must organise different types of information using variables, arrays, and dictionaries during workflow execution.

Learners are required to analyse the business data and determine suitable data structures for the automation solution.


PA0401 — Analyse Business Data for Automation

Tools/Resources

  • Business datasets
  • Workflow documentation
  • RPA platform

Activity Instructions

  1. Review the provided business data.
  2. Identify different types of information within the dataset.
  3. Analyse how the data will be processed during automation.
  4. Categorise data according to usage requirements.
  5. Record analysis findings.

Expected Outcome

Business data is analysed successfully for automation processing.

Evidence Required

  • Screenshot of analysed dataset
  • Screenshot of identified data categories
  • Screenshot of analysis notes

PA0402 — Identify and Configure Variables

Tools/Resources

  • RPA development environment
  • Workflow designer
  • Business dataset

Activity Instructions

  1. Identify values requiring variable storage.
  2. Select suitable variable types.
  3. Create variables within the workflow.
  4. Configure variable names and properties.
  5. Verify variable usage during execution.

Expected Outcome

Variables are identified and configured successfully.

Evidence Required

  • Screenshot of created variables
  • Screenshot of variable properties
  • Screenshot of workflow variable usage

PA0403 — Create and Use Arrays

Tools/Resources

  • RPA platform
  • Structured datasets
  • Workflow tools

Activity Instructions

  1. Identify grouped data suitable for arrays.
  2. Create arrays within the workflow.
  3. Store multiple values in arrays.
  4. Process array information during execution.
  5. Verify array outputs.

Expected Outcome

Arrays are created and used successfully within workflows.

Evidence Required

  • Screenshot of created arrays
  • Screenshot of stored array values
  • Screenshot of processed array outputs

PA0404 — Create and Use Dictionaries

Tools/Resources

  • RPA platform
  • Workflow designer
  • Business data

Activity Instructions

  1. Identify data suitable for dictionary structures.
  2. Create dictionaries using key-value pairs.
  3. Store and retrieve information from dictionaries.
  4. Process dictionary values during workflow execution.
  5. Verify processed outputs.

Expected Outcome

Dictionaries are created and processed successfully.

Evidence Required

  • Screenshot of created dictionaries
  • Screenshot of key-value structures
  • Screenshot of processed dictionary outputs

PA0405 — Test and Validate Data Structures

Tools/Resources

  • RPA platform
  • Test data
  • Debugging tools

Activity Instructions

  1. Execute workflows containing variables, arrays, and dictionaries.
  2. Validate processed outputs.
  3. Identify and correct data handling issues.
  4. Verify successful workflow execution.
  5. Save all completed workflow files.

Expected Outcome

Data structures are tested and validated successfully.

Evidence Required

  • Screenshot of workflow execution
  • Screenshot of validated outputs
  • Screenshot of corrected data handling issues

Key Notes

  • Variables store individual data values.
  • Arrays store grouped information.
  • Dictionaries store key-value data pairs.
  • Correct data structures improve workflow efficiency.
  • Data analysis improves automation accuracy.
  • Validation improves workflow reliability.
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