PREDICTIVE ANALYTICS

BUS5PA

2017

Credit points: 15

Subject outline

Predictive analytics refers to a variety of statistical, data mining and analytical techniques used to develop models that predict future events from data collected. This unit will provide students with the knowledge and skills to build predictive models and use data mining tools in real business scenarios. Students will be given the opportunity to gain hands-on experience with one of the globally most widely used predictive analytics software tools. Case studies from diverse fields such as business, finance, marketing, health and social sciences will be used to demonstrate the value of predictive analytics. A number of related data mining and machine learning techniques such as neural networks, decision trees, market basket analysis, association rule generation, customer segmentation and profiling will also be taught to provide the background data modelling for building predictive models. The effect of big data, stream analysis and text analytics on traditional predictive techniques will also be discussed.

SchoolLa Trobe Business School

Credit points15

Subject Co-ordinatorDamminda Alahakoon

Available to Study Abroad StudentsNo

Subject year levelYear Level 5 - Masters

Exchange StudentsNo

Subject particulars

Subject rules

Prerequisites BUS5PB; or enrolled in SMDS or HMSA or HGSA or LMMKT or LMMMT or LMMSM

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsData Mining TechniquesRecommendedLinoff and Berry, 2011Wiley
ReadingsData Science for Business: What you need to know about data mining and data analytic thinkingRecommendedProvost and FawcettO'Reilly Media

Graduate capabilities & intended learning outcomes

01. Understand the key statistical theories and data mining techniques for predictive analytics

Activities:
Lectures and workshops
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving)
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)

02. Learn to use predictive analytics technology using commercial tools to solve business problems

Activities:
Lectures, workshops and assignments
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving)
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)

03. Learn the current techniques, trends in predictive analytics as well as the environment in which predictive analytics is used

Activities:
Lectures, workshops and assignments
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving)
Personal and Professional Skills (Study and Learning Skills)
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)

Subject options

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Start date between: and    Key dates

City Campus, 2017, Semester 2, Night

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorDamminda Alahakoon

Class requirements

Lecture/Workshop Week: 31 - 43
One 3.0 hours lecture/workshop per week at night from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
Assignment, building and validating predictive models30 01, 02
Assignment, application and evaluation of predictive models30 02, 03
Assignment, market basket analysis and association rules40 01, 02, 03