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.
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.
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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.
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.
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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.
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 Overview

This lesson introduces learners to software tools and techniques used for analysing and visualising data in business and automation environments. Learners will explore reporting tools, tables, dashboards, pivot tables, charts, data models, and methods used to import and organise information from different sources. The lesson also examines how organisations use data visualisation to improve decision-making, reporting, and operational efficiency.

Lesson Outcomes

After completing this lesson, learners will be able to:

  • Explain the importance of data analysis and visualisation
  • Describe reporting tools and techniques
  • Explain the use of tables, pivot tables, and pivot charts
  • Describe dashboards and data models
  • Explain methods used to import data
  • Describe how measures and visualisations are created
  • Explain how data visualisation supports decision-making

KT0501: Reporting

Reporting involves organising and presenting information in a structured format to support decision-making and business operations.

Reports help organisations to:

  • Monitor performance
  • Analyse trends
  • Identify problems
  • Track business activities
  • Support planning and decision-making

Reports may include:

  • Sales reports
  • Financial reports
  • Inventory reports
  • Customer reports
  • Operational reports

Good reports should be:

  • Accurate
  • Clear
  • Organised
  • Relevant
  • Easy to understand

Modern reporting systems often generate reports automatically using databases and automation technologies.

In RPA environments, bots may automatically generate and distribute reports.


KT0502: Tables

Tables organise information into rows and columns to make data easier to read and analyse.

Example table:

Product Price Quantity
Laptop R12 000 10
Printer R3 500 5

Tables are useful because they:

  • Organise information clearly
  • Simplify comparisons
  • Support calculations
  • Improve readability

Tables are widely used in:

  • Databases
  • Spreadsheets
  • Reports
  • Dashboards

Structured tables improve data processing and automation efficiency.


KT0503: Pivot Tables and Pivot Charts

Pivot Tables

Pivot tables are tools used to summarise and analyse large amounts of data quickly.

Pivot tables help users to:

  • Group information
  • Calculate totals
  • Compare values
  • Filter data
  • Identify trends

Example:

Department Total Sales
Electronics R50 000
Furniture R30 000

Pivot tables are commonly used in spreadsheet software such as Microsoft Excel.

Pivot Charts

Pivot charts provide graphical visualisations of pivot table information.

Charts help users identify:

  • Patterns
  • Trends
  • Comparisons
  • Performance changes

Common chart types include:

  • Bar charts
  • Pie charts
  • Line graphs
  • Column charts

Pivot tables and charts improve business analysis and reporting efficiency.


KT0504: Dashboards

Dashboards are visual displays that present important business information in a single view.

Dashboards combine:

  • Charts
  • Tables
  • Graphs
  • Indicators
  • Reports

Dashboards allow organisations to monitor:

  • Performance
  • Sales
  • Productivity
  • Financial information
  • Operational activities

Good dashboards are:

  • Easy to understand
  • Interactive
  • Visually organised
  • Updated regularly

Dashboards improve decision-making because users can quickly identify trends and issues.

In automation environments, dashboards may display real-time information generated by automated systems and bots.


KT0505: Hierarchies and Time Data

Hierarchies

Hierarchies organise data into levels.

Example hierarchy:

  • Year
    • Month
      • Day

Hierarchies improve data organisation and simplify analysis.

Time Data

Time data refers to information related to dates and time periods.

Examples include:

  • Daily sales
  • Monthly revenue
  • Annual performance

Time-based analysis helps organisations:

  • Identify trends
  • Forecast future activities
  • Compare performance over time

Business intelligence systems often use hierarchies and time data for reporting and dashboards.


KT0506: The Data Model

A data model is a structured representation of how data is organised and related within a system.

Data models define:

  • Tables
  • Relationships
  • Data fields
  • Data flow

Good data models improve:

  • Database performance
  • Reporting accuracy
  • Data consistency
  • Automation efficiency

Data models are important because they organise information in ways that support business operations and analysis.


KT0507: Importing Data from Files

Organisations often import data from external files into reporting and analysis systems.

Common file formats include:

  • CSV files
  • Excel spreadsheets
  • Text files
  • JSON files

Importing data allows organisations to:

  • Consolidate information
  • Process external data
  • Analyse reports
  • Support automation

Automation systems and RPA bots may import data automatically to support workflows and reporting processes.


KT0508: Importing Data from Databases

Business systems often retrieve information directly from databases for reporting and analysis.

Importing data from databases allows organisations to:

  • Access real-time information
  • Combine data from multiple systems
  • Improve reporting accuracy
  • Support business intelligence

Common database systems include:

  • MySQL
  • SQL Server
  • Oracle
  • PostgreSQL

Database integration improves automation and operational efficiency.


KT0509: Importing Data from Reports

Reports generated by other systems may also be imported for analysis and visualisation.

Examples include:

  • Financial reports
  • Operational reports
  • Sales reports
  • Inventory reports

Imported report data can be analysed to identify:

  • Trends
  • Performance issues
  • Opportunities for improvement

KT0510: Creating and Formatting Measures

Measures are calculations used in reporting and business intelligence systems.

Measures may include:

  • Totals
  • Averages
  • Percentages
  • Profit calculations
  • Growth calculations

Example:

Measure Formula
Total Sales Sum of sales values
Average Revenue Total revenue ÷ number of transactions

Formatting measures improves readability and reporting accuracy.

Examples of formatting include:

  • Currency formatting
  • Percentage formatting
  • Decimal formatting

Measures support data-driven decision-making.


KT0511: Visualising Data

Data visualisation refers to the graphical representation of information to make analysis easier and more understandable.

Common visualisation tools include:

  • Charts
  • Graphs
  • Dashboards
  • Heat maps
  • Tables

Benefits of data visualisation include:

  • Faster understanding of information
  • Improved decision-making
  • Better trend analysis
  • Simplified reporting

Good visualisations should be:

  • Clear
  • Accurate
  • Relevant
  • Visually organised

In business and RPA environments, visualisation tools help organisations monitor performance and improve operational efficiency.


Key Notes

  • Reporting presents information in structured formats to support decision-making.
  • Tables organise information into rows and columns for easier analysis.
  • Pivot tables summarise and analyse large data sets efficiently.
  • Dashboards display important information visually in a single view.
  • Hierarchies and time data improve trend analysis and reporting.
  • Data models organise relationships between information.
  • Data can be imported from files, databases, and reports.
  • Measures perform calculations used in reporting systems.
  • Data visualisation improves understanding and business analysis.
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