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.

2026 10

Campus offering

No unit offerings are currently available for this unit.

Prerequisites

Nil

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

Apply how data science and AI empower organisations to make informed and strategic decisions while driving innovation and improving operational efficiency.
Relevant Graduate Capabilities: GC2, GC10

Outline the stages of the data science process, in...

Learning Outcome 02

Outline the stages of the data science process, including data collection, visualisation, statistical analysis, and the theoretical applications of machine learning techniques, such as classification, regression, and clustering.
Relevant Graduate Capabilities: GC1, GC10

Analyse the application of AI concepts across dive...

Learning Outcome 03

Analyse the application of AI concepts across diverse industries, including digital marketing, financial services, and supply chain management, using case studies and practical examples to illustrate real-world impact and innovation.
Relevant Graduate Capabilities: GC2, GC8

Utilise GenAI to write and modify basic Python scr...

Learning Outcome 04

Utilise GenAI to write and modify basic Python scripts, perform data analysis, and generate insights to address fundamental business questions programmatically.
Relevant Graduate Capabilities: GC2, GC10

Design and interpret visual representations of dat...

Learning Outcome 05

Design and interpret visual representations of data distributions, trends, and correlations to communicate insights effectively and support business strategies.
Relevant Graduate Capabilities: GC11, GC12

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

Weighting

25%

Learning Outcomes LO1, LO2, LO5
Graduate Capabilities GC1, GC2, GC10

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

Weighting

35%

Learning Outcomes LO1, LO2, LO3, LO4
Graduate Capabilities GC2, GC8, GC10, GC11, GC12

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:

  1. Writing or modifying Python scripts using GenAI for basic data cleaning and analysis.
  2. Generating insights from the analysed data and presenting findings in a visual format (charts, graphs).
  3. 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

Weighting

40%

Learning Outcomes LO2, LO3, LO4, LO5
Graduate Capabilities GC2, GC8, GC10, GC11

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.

Representative texts and references

Representative texts and references

Arias, R., Martinez, G., Cáceres, D., & Garces, E. (2024, April). Limitations and Benefits of the ChatGPT for Python Programmers and Its Tools for Evaluation. In Computer Science On-line Conference (pp. 171-194). Cham: Springer Nature Switzerland.

Burström, T., Parida, V., Lahti, T., & Wincent, J. (2021). AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research. Journal of Business Research127, 85-95.

Cui, Y. G., van Esch, P., & Phelan, S. (2024). How to build a competitive advantage for your brand using generative AI. Business Horizons.

Gentsch, P. (2018). AI in marketing, sales and service: How marketers without a data science degree can use AI, big data and bots. springer.

Kalita, J. K., Bhattacharyya, D. K., & Roy, S. (2023). Fundamentals of Data Science: Theory and Practice. Elsevier.

Kemp, A. (2024). Competitive advantage through artificial intelligence: Toward a theory of situated AI. Academy of Management Review49(3), 618-635.

Nandi, G., & Sharma, R. K. (2020). Data Science fundamentals and practical approaches: understand why data science is the next. BPB Publications.

Porter, L., & Zingaro, D. (2024). Learn AI-Assisted Python Programming: With Github Copilot and ChatGPT. Simon and Schuster.

Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research261(2), 626-639.

McKinsey & Company. (2025). AI in the workplace: Empowering businesses through automation and data-driven decision-making. Retrieved from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

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