PREDICTIVE ANALYTICS

BUS5PA

2021

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/Exchange StudentsNo

Subject year levelYear Level 5 - Masters

Available as ElectiveNo

Learning ActivitiesWeekly lectures where the concepts and theories are discussed and weekly practical hands on workshops using appropriate software to apply the theory to practical problems

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyMerit based quota management

Quota-conditions or rulesBy the order of application to subject coordinator.

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Readings

Predictive Modelling with SAS Enterprise Miner: Practical Solutions for Business Analytics Applications, 3rd eds

Resource TypeRecommended

Resource RequirementN/A

AuthorSarma

Year2017

Edition/Volume3rd

PublisherSAS Institute

ISBN978-1-62960-264-6

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Data mining for Business Analytics: Concepts, Techniques and Applications with JMP Pro

Resource TypeRecommended

Resource RequirementN/A

AuthorShmueli et al

Year2017

Edition/Volume1st

PublisherWiley & Sons

ISBN9781118879368

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Data Science for Business: What you need to know about data mining and data analytic thinking

Resource TypeRecommended

Resource RequirementN/A

AuthorProvost and Fawcett

Year2013

Edition/VolumeN/A

PublisherO'Reilly Media

ISBN978-1-4493-6132-7

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedYes

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Appraise and differentiate the key statistical theories and data mining techniques focusing on their suitability to solve predictive analytics problems
02. Compare and contrast different predictive analytics models and data mining techniques to formulate solutions for business problems.
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.

Subject options

Select to view your study options…

Start date between: and    Key dates

City Campus, 2021, Semester 1, Night

Overview

Online enrolmentYes

Maximum enrolment size120

Subject Instance Co-ordinatorDamminda Alahakoon

Class requirements

Lecture/WorkshopWeek: 10 - 22
One 3.00 h lecture/workshop per week on weekdays at night from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*

Building and Evaluating Predictive Models 1500 word equivalence

N/AOtherGroupNo30 SILO1, SILO2

Cluster Analysis and Predictive Modelling 1500 word equivalence

N/AOther written examIndividualNo40 SILO2, SILO3

Predictive Analytics Case Study 2000 word equivalence

N/AAssignmentIndividualNo30 SILO1, SILO2, SILO3

City Campus, 2021, Semester 2, Night

Overview

Online enrolmentYes

Maximum enrolment size120

Subject Instance Co-ordinatorDamminda Alahakoon

Class requirements

Lecture/WorkshopWeek: 31 - 43
One 3.00 h lecture/workshop per week on weekdays at night from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*

Building and Evaluating Predictive Models 1500 word equivalence

N/AOtherGroupNo30 SILO1, SILO2

Cluster Analysis and Predictive Modelling 1500 word equivalence

N/AOther written examIndividualNo40 SILO2, SILO3

Predictive Analytics Case Study 2000 word equivalence

N/AAssignmentIndividualNo30 SILO1, SILO2, SILO3