bus5pa predictive analytics
PREDICTIVE ANALYTICS
BUS5PA
2020
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 ActivitiesN/A
Capstone subjectNo
Subject particulars
Subject rules
Prerequisites Students must have completed (BUS5PB or BUS5DWR) or Students must be admitted in one of the following courses: SMDS, HMSA, HGSA
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
Learning resources
Data mining for Business Analytics: Concepts, Techniques and Applications with JMP Pro
Resource TypeBook
Resource RequirementRecommended
AuthorShmueli et al
Year2016
Edition/VolumeN/A
PublisherWiley & Sons
ISBN978-1-118-877432
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/A
Predictive Modelling with SAS Enterprise Miner: Practical Solutions for Business Analytics Applications, 3rd eds
Resource TypeBook
Resource RequirementRecommended
AuthorSarma
Year2017
Edition/VolumeN/A
PublisherSAS Institute
ISBN978-1-62960-264-6
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 TypeBook
Resource RequirementRecommended
AuthorProvost and Fawcett
YearN/A
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-focusedNo
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
Subject options
Select to view your study options…
City Campus, 2020, Semester 1, Night
Overview
Online enrolmentYes
Maximum enrolment size120
Subject Instance Co-ordinatorDamminda Alahakoon
Class requirements
Lecture/WorkshopWeek: 10 - 22
One 3.00 hours lecture/workshop per week on weekdays at night from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Building and Evaluating Predictive Models1500 word equivalence | N/A | N/A | No | 30 | SILO1, SILO2 |
Cluster Analysis and Predictive Modelling1500 word equivalence | N/A | N/A | No | 30 | SILO2, SILO3 |
Predictive Analytics Case Study2000 word equivalence | N/A | N/A | No | 40 | SILO1, SILO2, SILO3 |
City Campus, 2020, Semester 2, Night
Overview
Online enrolmentYes
Maximum enrolment size120
Subject Instance Co-ordinatorDamminda Alahakoon
Class requirements
Lecture/WorkshopWeek: 31 - 43
One 3.00 hours lecture/workshop per week on weekdays at night from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Building and Evaluating Predictive Models1500 word equivalence | N/A | N/A | No | 30 | SILO1, SILO2 |
Cluster Analysis and Predictive Modelling1500 word equivalence | N/A | N/A | No | 30 | SILO2, SILO3 |
Predictive Analytics Case Study2000 word equivalence | N/A | N/A | No | 40 | SILO1, SILO2, SILO3 |