Statistics and Predictive Analytics
About This Course
This module covers descriptive analytics, probability distributions, hypothesis testing, correlation, and regression before advancing through the CRISP-DM framework for predictive modelling. Students begin with visual workflow tools and progress to manual model building, developing the ability to evaluate models across accuracy, cost, complexity, and interpretability dimensions. AutoML tools are introduced as a productivity layer. Generative AI is applied throughout to clarify statistical concepts, verify modelling assumptions, assist with code generation, and translate model findings into plain business language. Students graduate from the module with a structured, repeatable approach to building and communicating predictive analytics solutions.
What You'll Learn
• Apply descriptive analytics, hypothesis testing, correlation, and regression in business contexts
• Build and evaluate predictive models across accuracy, cost, complexity, and interpretability using the CRISP-DM framework, including AutoML as a productivity layer
• Apply generative AI to clarify statistical concepts, verify assumptions, support code generation, and translate findings into business recommendations
• Critically evaluate and refine AI-generated analytical outputs using sound statistical judgement
Entry Requirements
Applicants should possess a relevant (Business, Engineering, or Technology) local Polytechnic Diploma, ITE Technical Diploma / Technical Engineer Diploma / Work-Study Technical Diploma or Degree.
Applicants who do not meet the entry requirements may be considered for admission based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted may be required to go through an interview and/or entrance test.