Unit rationale, description and aim
In today’s rapidly evolving business landscape, the ability to harness data and leverage artificial intelligence (AI) is no longer optional; it is essential. This unit introduces the transformative potential of data science and AI in business contexts, serving as a foundational component of the BBUS. It complements units in business analytics and strategic management by providing hands-on experience with AI applications in real-world scenarios.
Students will develop practical competencies in applying data-driven methodologies, leveraging AI tools for strategic decision-making, and critically assessing the ethical implications of AI in business contexts. The unit focuses on tangible business applications, including generative AI, which enables students to generate Python scripts for business tasks and run them in user-friendly technologies requiring no prior technical expertise. Through interactive projects and case studies, students will explore the data science life cycle, learn data visualisation techniques, and apply AI-driven solutions to areas such as marketing, operations, and finance.
The unit also emphasises the ethical implications of using AI in business, fostering a commitment to responsible practices. By acquiring these capabilities, students will be prepared to implement AI solutions, drive innovation, and bridge the gap between technology and business strategy, ensuring their readiness for a data-driven business environment.
Campus offering
No unit offerings are currently available for this unit.Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Apply how data science and AI empower organisation...
Learning Outcome 01
Outline the stages of the data science process, in...
Learning Outcome 02
Analyse the application of AI concepts across dive...
Learning Outcome 03
Utilise GenAI to write and modify basic Python scr...
Learning Outcome 04
Design and interpret visual representations of dat...
Learning Outcome 05
Content
Topics will include:
- Applying data science and AI to enhance business decision-making
- Leveraging Jupyter Notebooks, Python, and GenAI for real-world data analysis
- Statistical foundations for AI and data science in business contexts
- Understanding the data science pipeline: essential steps and methods
- Transforming data into actionable business insights through visualisation
- Addressing the ethical implications of AI and data science in business
- Shaping future business leaders for AI-focused careers across industries
Assessment strategy and rationale
Assessments are used primarily to foster learning. ACU adopts a constructivist approach to learning which seeks alignment between the fundamental purpose of each unit, the learning outcomes, teaching and learning strategy, assessment and the learning environment. In order to pass this unit, students are required to achieve an overall score of at least 50%. Using constructive alignment, the assessment tasks are designed for students to demonstrate their achievement of each learning outcome.
Each of these assessment pieces has been meticulously designed to enhance students' abilities, promote greater inclusivity, and expand their skillsets. These assessments integrate key elements from the unit’s curriculum and objectives, offering opportunities to apply unit concepts in real-world business settings. Assessment one is an individual report that requires students to critically evaluate the role of data science and AI in driving business innovation and operational efficiency. It encourages students to assess how these technologies can empower organisations and transform decision-making processes. Assessment two, a group-based task, fosters collaboration as students work together to develop an AI solution tailored to specific business challenges. The final assessment provides a hands-on opportunity for students to practically apply AI tools to solve business problems.
Overview of assessments
Assessment Task 1: Leveraging Data Science and AI...
Assessment Task 1: Leveraging Data Science and AI to Drive Business Innovation
Students will write a 1,200-word report that includes the run of AI program/tools, to explain how data science and AI empower organisations to make strategic decisions while driving innovation and operational efficiency. The report should:
· Examine the core concepts of the data science process and their significance in addressing business challenges.
· Illustrate how organisations have successfully leveraged data science and AI to drive innovation and achieve measurable outcomes.
· Critically evaluate challenges and propose practical solutions for implementing data science and AI in business.
· Reflect on how this knowledge shapes their understanding of strategic decision-making in a data-driven world.
Submission Type: Individual
Assessment Method: Report and program files
Artefact: Written report and run of the program files
25%
Assessment Task 2: Strategic AI Solution for Busi...
Assessment Task 2: Strategic AI Solution for Business Challenges
Students will work in teams to develop an AI integration plan for a specific business function, such as marketing, finance, or operations. The deliverables include a 1000-word written report and a 10-minute group presentation.
The assessment requires students to identify a business problem within the chosen function and propose an AI-driven solution. The report should outline how the solution addresses the problem, detail the stages of implementation and highlight potential challenges with mitigation strategies.
Submission Type: Group
Assessment Method: Report and Presentation
Artefact: Video Presentation and Written Report
35%
Assessment Task 3: Hands-On AI: Problem-Solving w...
Assessment Task 3: Hands-On AI: Problem-Solving with Python and GenAI
Students will complete a series of practical exercises to demonstrate their ability to utilise GenAI and Python for data analysis and visualisation within a business context. Tasks include:
- Writing or modifying Python scripts using GenAI for basic data cleaning and analysis.
- Generating insights from the analysed data and presenting findings in a visual format (charts, graphs).
- Reflecting on the process in a 500-word write-up, addressing challenges and key learnings.
Submission Type: Individual
Assessment Method: Practical Exercises (30%) and Reflection (10%)
Artefact: Combined Document Submission
40%
Learning and teaching strategy and rationale
Students should anticipate undertaking 150 hours of study for this unit over a twelve-week semester or equivalent study period, including class attendance, readings, online forum participation, and assessment preparation.
This unit is delivered in both “Attendance” and “Online” modes to accommodate diverse learning needs and preferences, ensuring students progressively build their skills in AI-driven business applications throughout the semester.
Attendance Mode
Students will participate in structured face-to-face learning sessions in designated blocks determined by the school. These sessions will integrate active learning strategies to scaffold knowledge development, allowing students to engage in hands-on experiences such as using generative AI tools and interpreting business data. The unit is designed with required upfront preparation before workshops, ensuring students come equipped with foundational concepts that are reinforced through practical applications. Online learning platforms will provide multiple opportunities for practice, revision, and skill reinforcement.
Online Mode
This unit adopts an active learning approach, enabling students to progressively develop key competencies through e-module activities, structured readings, and guided reflections. Learning is scaffolded across the semester to facilitate incremental skill development, with students engaging in collaborative online environments, including workshops, discussion forums, chat rooms, and webinars. Practical applications, such as using AI tools for data interpretation, will be integrated into pre-recorded lectures and interactive e-modules. In addition, curated electronic readings will support conceptual understanding and ensure alignment with unit learning outcomes.