Lesson Summary:
This lesson covers workplace induction activities, basic data scraping tasks, and reviewing AI solutions within a professional environment. Learners will understand company procedures, perform supervised data gathering, and analyse existing AI systems.
Lesson 1: Workplace Induction, Data Collection & AI Solution Review (WE01–WE03)
This lesson introduces learners to their initial responsibilities in the workplace environment as part of their AI development role. These activities reflect real tasks performed by junior AI technicians and data practitioners in South African organisations.
⭐ WE01: Workplace Induction & Orientation
Learners are required to:
- Complete organisational induction processes
- Understand company policies and workplace conduct
- Meet supervisors, mentors, and team members
- Observe organisational workflows and communication channels
- Understand the AI department’s purpose and responsibilities
This step prepares the learner for safe, compliant, and effective participation in the workplace.
⭐ WE02: Data Collection Using Data Scraping Techniques
Learners will:
- Use supervised processes to gather data from approved sources
- Apply scraping or extraction tools (e.g., Python scripts, APIs, CSV exports)
- Collect structured or unstructured datasets
- Validate the quality of captured data
- Document sources and extraction procedures
Data scraping prepares learners for later AI solution development.
⭐ WE03: Review Existing AI Solutions Within the Workplace
Learners participate in reviewing current AI solutions by:
- Observing the performance of existing AI tools
- Identifying strengths and weaknesses
- Understanding data flow and model behaviour
- Reviewing model outputs with team members
- Documenting findings for reporting
- Providing supervised feedback based on observations
This task introduces learners to real-world AI systems and evaluation processes.