Tools and Techniques for Business Artificial Intelligence
Overview
This unit establishes foundational Python programming and data manipulation skills essential for developing AI-enabled business solutions. Students will gain practical experience with critical tools and techniques for business AI applications, enabling them to understand, create, and implement AI-driven automation processes. Through hands-on, industry-relevant projects, students will develop technical capabilities in Python, data handling, and basic AI operations that directly support business decision-making and process automation. This unit provides the technical foundation needed for subsequent courses in the degree program focusing on AI applications and digital transformation.
Requisites
OR
Completion of a bridging module for students without prior coding experience. This is subject to approval from the Course Director or nominated academic staff member
01-November-2026
Unit learning outcomes
Students who successfully complete this unit will be able to:
- Apply Python programming fundamentals including control structures, functions, and data structures to develop solutions for business AI applications
- Process, transform, and analyze business data using Python libraries to prepare datasets for AI operations and extract actionable insights
- Implement basic AI automation tools and techniques to streamline business processes and enhance decision-making
- Develop and evaluate data pipelines that connect business data sources to AI systems with appropriate documentation
- Utilize visualization techniques to effectively communicate data insights and AI application results to diverse stakeholders
- Collaborate effectively in developing, testing, and communicating Python-based AI solutions for business contexts
Teaching methods
Hawthorn
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
On-campus ( Class 1 (Computer Labs) |
2 | 12 weeks | 24 |
Online Lecture (asynchronous) |
1 | 12 weeks | 12 |
Unspecified Activities Various |
9.5 | 12 weeks | 114 |
| Total | 150 |
Assessment
| Type | Task | Weighting | ULOs |
|---|---|---|---|
| Portfolio | Individual | 30% | 1,2,4 |
| Portfolio | Group | 30-50% | 2,3,4,6 |
| Project | Project | 10-20% | 3,5,6 |
Content
- Python fundamentals for AI applications: syntax, control structures, functions, and data manipulation.
- Data preparation techniques for AI: collection, cleaning, and transformation of business data formats (CSV, Excel, JSON, APIs).
- Business data analysis and manipulation using Python libraries (NumPy, Pandas) for AI applications.
- Development of data pipelines to connect business systems with AI tools.
- Data visualization techniques for communicating AI insights (Matplotlib, Seaborn).
- Business process automation and workflow optimization using Python and basic AI tools.
- Introduction to AI libraries and frameworks for business applications (scikit-learn, TensorFlow basics).
- AI operations (AIOps) fundamentals and implementation approaches.
- Ethical considerations in AI applications for business.
- Version control, documentation, and collaborative development practices for AI projects.
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.