Artificial Intelligence in Business Decision Making
Overview
This unit is designed to empower students to leverage the power of data science and AI to derive actionable intelligence from business data. Students will gain practical skills in data wrangling, exploratory data analysis, and visualisation using a versatile and widely adopted language for data science. Scaffolded upon these skills, the unit will introduce machine learning concepts for generating predictive insights and foresights to support effective data-driven business decision-making. The overarching aim is to equip future business professionals with the data literacy and analytical capabilities necessary to support effective, data-driven decision-making for organisations.
Requisites
AND
COS10009 Introduction to Programming
AND
INF10029 Tools and Techniques for Business Artificial Intelligence
01-November-2026
Unit learning outcomes
Students who successfully complete this unit will be able to:
- Demonstrate an understanding of the strategic value of data science and AI in driving data-driven business decision-making
- Contextualise business problems to determine appropriate data requirements to generate analytical insight and foresight
- Apply the appropriate data science principles and techniques to analyse organisational datasets and provide data-driven insights to an organisation’s complex problems
- Evaluate the technological, organisational, ethical, and managerial considerations influencing the implementation and adoption of Data Science or AI-enabled analytics solutions
- Collaborate effectively in a team to interpret analytical findings and communicate actionable business insights using appropriate visual and narrative techniques
Teaching methods
All applicable locations
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
On-campus Class |
2.00 | 12 weeks | 24 |
Online Lecture (asynchronous) |
1.00 | 12 weeks | 12 |
Unspecified Activities Independent Learning |
9.5 | 12 weeks | 114 |
| Total | 150 |
Assessment
| Type | Task | Weighting | ULOs |
|---|---|---|---|
| Assignment 1 | Individual | 30-50% | 1,2,3 |
| Assignment 2 | Group | 30-50% | 3,4 |
| Assignment 3 | Individual | 10-20% | 4,5 |
Content
- Strategic Value of Data Science, Data-Driven Decisions, Data Science Process
- Python Fundamentals, Core Data Science Libraries (e.g., NumPy, Pandas)
- Data Wrangling and Preprocessing with Python
- Exploratory Data Analysis (EDA) and Visualisation with Python
- Introduction to Machine Learning Concepts and Workflow
- Regression Techniques for Business Prediction
- Classification Techniques for Business Categorisation
- Ethical and Practical Considerations in Business Data Science
- Communicating Data Insights for Business Decisions
- Emerging Trends in Business Data Science and AI
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.