This assessment seeks to simulate a real-world task that you may have to undertake in the future. Therefore, the assignment is non-prescriptive and requires you to pose a relevant, small, creative and significant problem to solve that could result in benefits to the organisation of choice.
In this assessment, you need to consider an organisation in an industry of your choice and articulate the steps this organisation needs to take to enable Machine Learning and/or AI for data-driven decision making. You are required to analyse a sample data set to demonstrate expected AI/ML outcomes.
You need to be familiar with the organisation and industry (e.g., where you have worked or are working, a future start-up company), NOT an organisation such as Amazon/Boeing/Qantas etc.
The report should address:
o Why AI would help this organisation given their current operations
o What Machine Learning techniques you would recommend
o An example of the predictive model using sample data
o Deployment considerations for the model
o The benefits for the organisation clearly articulated with estimates of expected revenue/profits or Return on Investment
Page 2 Kaplan Business School Assessment Outline
• By Week 9 identify a company and industry you are familiar with that would benefit from Machine Learning/AI. Note:
o The application needs to be based on Machine Learning/AI (not some other aspect of analytics).
o Focus on a single, well defined (small) application.
o Sample datasets maybe sourced from:
an organisation if you work there
public repositories such as kaggle.com and https://archive.ics.uci.edu/ml/datasets.php,
Open government data such as abs.gov.au
• By Week 12 draft some preliminary points pertaining to the report in class. You are encouraged to consider the current mode of operation, possible inefficiencies, available data and how this data may be used to provide efficiencies based on the concepts and techniques covered in the subject. Think of yourself as a consultant or a founder.
• Your facilitator will advise on the appropriateness of your choice and proposed methodology with regard to the requirements for the assessment.