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.
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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.
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.
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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.
0/3
Occupational Certificate: Robotic Process Automation (RPA) Developer

Lesson Overview

This lesson introduces learners to different ways of expressing very large and very small numbers in mathematics, science, and computing environments. Learners will explore scientific notation, metric prefixes, and unit conversions used in technology, data systems, and digital environments.

Lesson Outcomes

After completing this lesson, learners will be able to:

  • Express numbers using scientific notation
  • Interpret and apply metric prefixes
  • Convert values between units of measurement
  • Explain the importance of size and magnitude in computing and technology
  • Perform basic scientific and measurement conversions

KT0301: Use Scientific Notation for Small and Large Numbers

Scientific notation is a mathematical method used to express very large or very small numbers in a shorter and simpler form.

Scientific notation is written in the following format:

                          a × 10ⁿ

Where:

  • a is a number greater than or equal to 1 but less than 10
  • n is the exponent showing how many places the decimal point moves

Scientific notation makes calculations easier when working with extremely large or small values.


Large Numbers

Large numbers are expressed using positive exponents.

Example 1

5000000 = 5 × 10⁶

Explanation:
The decimal point moves 6 places to the left.


Example 2

320000000 = 3.2 × 10⁸


Small Numbers

Small numbers are expressed using negative exponents.

Example 3

0.00045 = 4.5 × 10⁻⁴

Explanation:
The decimal point moves 4 places to the right.


Example 4

0.00000012 = 1.2 × 10⁻⁷


Scientific notation is widely used in:

  • Computing
  • Engineering
  • Physics
  • Data storage
  • Telecommunications
  • Scientific research

In computing, scientific notation helps represent:

  • Processor speeds
  • Storage capacities
  • Data transfer rates
  • Very small electrical measurements

Using scientific notation improves readability and simplifies calculations involving very large or very small values.


KT0302: Prefixes — From Giga to Pica (10⁹ to 10⁻¹²)

Metric prefixes are used to represent different sizes and magnitudes of measurement units.

Prefixes simplify communication when working with large or small quantities.


Common Metric Prefixes

Prefix Symbol Value
Giga G 10⁹
Mega M 10⁶
Kilo k 10³
Base Unit 10⁰
Milli m 10⁻³
Micro µ 10⁻⁶
Nano n 10⁻⁹
Pica p 10⁻¹²

Examples in Technology

Gigabytes (GB)

Used to measure large storage capacities.

Example:

  • 1 GB = 10⁹ bytes

Megabytes (MB)

Used for file sizes and memory measurements.

Example:

  • 1 MB = 10⁶ bytes

Kilobytes (KB)

Used for smaller files and data units.

Example:

  • 1 KB = 10³ bytes

Nanoseconds (ns)

Used to measure extremely small time intervals in computing and electronics.

Example:

  • Processor speeds are often measured in nanoseconds.

Understanding prefixes is important in:

  • Data storage
  • Networking
  • Electronics
  • Engineering
  • Programming
  • Telecommunications

Incorrect interpretation of units and prefixes may result in:

  • Storage calculation errors
  • Incorrect data transfers
  • System configuration problems

Learners working in digital environments must be comfortable interpreting sizes, capacities, and measurements accurately.


KT0303: Conversions — SI to Imperial: Degrees Fahrenheit to Degrees Celsius

Different countries and industries use different measurement systems. The International System of Units (SI) is commonly used worldwide, while some countries still use Imperial measurements.

Understanding unit conversions is important in:

  • Science
  • Engineering
  • Computing
  • Data analysis
  • Automation systems

One common conversion is temperature conversion between:

  • Degrees Celsius (°C)
  • Degrees Fahrenheit (°F)

Fahrenheit to Celsius Formula

                   °C = ((°F−32)×5)/9


Example 1

Convert 68°F to Celsius.

Step 1

                  68 − 32 = 36 

Step 2

                  36 × 5 = 180 

Step 3

                  180 ÷ 9 = 20 

Final Answer:

                  68°F = 20°C


Celsius to Fahrenheit Formula

                  °F = (°C × 9/5) + 32


Example 2

Convert 25°C to Fahrenheit.

Step 1

                  25 × 9/5 = 45

Step 2

                  45 + 32 = 77 

Final Answer:

                  25°C = 77°F


Unit conversions are important in computing and automation because systems may:

  • Receive data from different countries
  • Use different measurement standards
  • Require standardised outputs
  • Process sensor measurements

Automation systems often rely on accurate unit conversions to ensure correct system performance and reporting.

Understanding size, magnitude, and conversions helps learners work more effectively with technical data and digital systems.

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