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BUSANA 7001 - Predictive and Visual Analytics

Career: Postgraduate Coursework
Units: 3
Term: 4210
Campus: North Terrace
Contact: Up to 3 hours per week
Available for Study Abroad and Exchange: Yes
Available for Non-Award Study: Yes
Pre-Requisite: CORPFIN 7033
Assessment: Exam/assignments/tests/tutorial work as prescribed at first lecture
Syllabus:

This course allows students to develop hands-on experience in constructing prediction models to assist business decision-making. Both traditional econometric models and emerging AI methods will be utilized. The course will also focus on representing data that appeals to the human visual-perceptual system. This includes utilising visual analytics tools to explore and analyse complex data sets using multivariate, text-based, geospatial, network, and graph-based data.

Course Fees

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Student Status

Domestic
International

What type of place are you studying in

Commonwealth supported
Full fee paying

Study Level

Undergraduate
Postgraduate Coursework
Non Award

Program of Study

Study Abroad student tuition fees are available here

Only some Postgraduate Coursework programs are available as Commonwealth Supported. Please check your program for specific fee information.

The fees displayed below for international students are for students commencing a program in 2024 only. International students who commenced a program in 2023 or prior can find their fee here.

Units
 EFTSL
Amount
3
 0.125
          
  


Course Outline

A Course Outline which includes Learning Outcomes, Learning Resources, Learning & Teaching for this course may be accessed here


Critical Dates

Term Last Day to Add Online Census Date Last Day to WNF Last Day to WF
4210 Tue 15/03/2022 Thu 24/03/2022 Fri 06/05/2022 Fri 10/06/2022


Class Details

Enrolment Class: Seminar
Class Nbr Section Size Available Dates Days Time Location
16049 SE01 50 40 28 Feb - 4 Apr Monday 3pm - 6pm Lower Napier, LG24, Teaching Room
25 Apr - 6 Jun Monday 3pm - 6pm Lower Napier, LG24, Teaching Room
Note: Please note, this a Mixed Cohort Learning tutorial, you may attend this tute either face to face or online. Once enrolled please refer to MyUni for more information.