HANDBOOK - Wellington Institute of Technology

Topic 1:Overview – analysis, analytics and intelligenceLearning Outcome(s):Understand the principles behind data and analysisAssessment criteria:1.1 Discuss why analytics matter1.2 Manage and explore data1.3 Use data access and data management to build analytical data martsTopic 2:Basics of Business AnalyticsLearning Outcome(s):Demonstrate knowledge and understanding of business intelligenceAssessment criteria:2.1 Define and use business analytics terminology2.2 Differentiate between classical statistics and business intelligence2.3 Identify data mining methodologies2.4 Conduct RFM (recency, frequency & monetary) analysisTopic 3:Predictive ModellingLearning Outcome(s):Apply predictive modellingAssessment criteria:3.1 Use decision trees3.2 Describe binary logistic regression concepts3.3 Conduct honest assessment and scoring3.4 Use predictive modelling to predict future behaviour and consequence of changeTopic 4:Experiment DesignLearning Outcome(s):Design and conduct analytical experimentsAssessment criteria:4.1 Describe one factor and multi factor experiments4.2 Explain blocking and orthogonality4.3 Conduct a multi factor experiment4.4 Gather and use continuous response data