PREDICTIVE ANALYTICS

BUS5PA

2018

Credit points: 15

Subject outline

The information age has combined with the widespread adoption of digital technology to turn information into a key business asset. Organizations now have access to massive volumes of data from diverse sources and require skills and expertise in making sense of this information for strategic decision making. Predictive analytics refers to a variety of statistical and analytical techniques used to develop models that predict future events from data. This subject will provide you with the knowledge and skills to build and use predictive models in real business scenarios. You will be given the opportunity to gain hands-on experience with one of the globally most widely used predictive analytics software tools. Case studies such as target marketing and customer churn analysis will be used to demonstrate the business value of predictive analytics. A number of related data mining and machine learning techniques such as neural networks, decision trees, customer segmentation and profiling will also be taught. 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. Appraise and differentiate the key statistical theories and data mining techniques focussing on their suitability to solve predictive analytics problems

Activities:
Weekly lectures where the concepts and theories are discussed and weekly practical hands on workshops using appropriate software to apply the theory to practical problems
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. Combine different predictive analytics models with data mining techniques to formulate solutions for business problems

Activities:
Weekly lectures where the concepts and theories are discussed and weekly practical hands on workshops using appropriate software to apply the theory to practical problems
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. Appraise the need of different predictive analytics models and techniques evaluate the value of such models and justify the inclusion of particular techniques in a case study

Activities:
Weekly lectures where the concepts and theories are discussed, reading material provided in LMS and weekly practical hands on workshops using appropriate software to apply the theory to practical problems
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, 2018, 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*
Assignment1: Building and Evaluating Predictive Models (1500 words)30 01, 02
Assignment2: Cluster Analysis and Predictive Modelling (1500 words)30 02, 03
Assignment3: Predictive Analytics Case Study (2000 words)40 01, 02, 03