PREDICTIVE ANALYTICS
BUS5PA
2016
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 subject will provide you with the knowledge and skills to build predictive models and use data mining tools 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 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, clustering and statistical regression 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.
School: La Trobe Business School
Credit points: 15
Subject Co-ordinator: Damminda Alahakoon
Available to Study Abroad Students: Yes
Subject year level: Year Level 5 - Masters
Exchange Students: Yes
Subject particulars
Subject rules
Prerequisites: BUS5PB
Co-requisites: BUS5SBF
Incompatible subjects: N/A
Equivalent subjects: N/A
Special conditions: N/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Data Mining Techniques | Recommended | Linoff and Berry, 2011 | Wiley |
| Readings | Data Science for Business: What you need to know about data mining and data analytic thinking | Recommended | Provost and Fawcett | O'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)
City Campus, 2016, Semester 2, Night
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Enrolment information:
Subject Instance Co-ordinator: Damminda Alahakoon
Class requirements
Lecture/WorkshopWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Assignment, building and validating predictive models | 1500 words equivalency task | 30 | 01, 02 |
| Assignment, application and evaluation of predictive models | 1500 words equivalency task | 30 | 02, 03 |
| Assignment, complete analytics case study | 2000 words equivalency task | 40 | 01, 02, 03 |