| | Learning outcome |
|---|
| 1 | Understand and apply key concepts, theories, and practices in data analysis, machine learning, statistics, and optimisation, demonstrating an integrated understanding of these fields |
|---|
| 2 | Apply business analytics techniques to evaluate complex business challenges autonomously, proposing data-driven and sustainable solutions |
|---|
| 3 | Communicate data analysis findings and recommendations using diverse modes and media, with emphasis on data visualisation, showcasing clarity, coherence, and persuasion in both written and oral forms |
|---|
| 4 | Demonstrate proficiency in using industry-standard tools and technologies for data collection, analysis, and presentation |
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| 5 | Foster creativity and innovation in solving business problems through the application of data analysis, machine learning, and optimisation techniques, driving responsible and sustainable change |
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| 6 | Integrate cross-cultural and multidisciplinary perspectives, including those of Aboriginal and Torres Strait Islanders stakeholders, in data-driven decision-making within various business contexts |
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| 7 | Merge knowledge and practices from data analytics, optimisation, and machine learning to address multifaceted business issues and facilitate interdisciplinary communication and collaboration |
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| 8 | Critically evaluate the social responsibilities of data professionals, ethical standards, professional integrity, and continuous learning and development |
|---|
| 9 | Employ data-driven insights to lead and support individuals and teams towards shared goals that foster innovation and sustainability, considering societal and environmental impacts. |
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