Course Code: ANL310
Synopsis
ANL310 Business Analytics Applications and Issues aims to equip students with knowledge of various applications of business analytics across different industries. The course covers a wide range of analytics applications, including defect prediction in manufacturing, cross-selling and up-selling for service providers and employee churn in the retail sector. Issues relating to model deployment and other important considerations, such as model latency, conditions for causality, and alternative explanations, are also discussed. Students are exposed to the responsible use of AI tools to support Python coding and to generate and critically evaluate alternative explanations that may affect the validity and reliability of model results.
Level: 3
Credit Units: 5
Presentation Pattern: EVERY JAN
Topics
- Fraud Detection
- Target Marketing
- Model Latency
- Oversampling
- Product Bundling
- Customer Segmentation
- Churn Modeling
- Deployment: Association versus Causality
- Conditions for Causality
- Placebo, Nocebo and Others
- Alternative Explanations
- Deployment Issues
Learning Outcome
- Compare the different modelling techniques used in different industries
- Demonstrate understanding of different analytics applications used in different industries
- Discuss issues related to the deployment of data mining models with the aid of AI
- Evaluate the applicability of different modelling techniques across different industries
- Recommend the appropriate analytics techniques to derive useful information to support decision-making for a variety of business problems
- Appraise the application of business analytics in different industries