Overview

This unit aims to develop and enhance students' conceptual and practical understanding of data analytics and artificial intelligence in the contemporary business context. Students will have an opportunity to immerse themselves in problem-solving activities requiring lateral and critical thinking by exploring structured and unstructured data, considering and applying the appropriate analytic and artificial intelligence techniques using a software programming environment for statistical computing (such as R) and visualisation tools and approaching organisational problems through data-driven decision-making processes. Further, students will learn fundamental concepts, key techniques and popular tools in artificial intelligence and how it is applied in business and industry.

Requisites

Prerequisites
INF30036 Business Analytics and Artificial Intelligence

Rule
200 credit points

Assumed Knowledge
Basic understanding of computer programming

Equivalent units
INF30030 Business Analytics
Teaching Periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 1
Location
Hawthorn
Start and end dates
26-February-2024
26-May-2024
Last self-enrolment date
10-March-2024
Census date
31-March-2024
Last withdraw without fail date
12-April-2024
Results released date
02-July-2024
Semester 2
Location
Hawthorn
Start and end dates
29-July-2024
27-October-2024
Last self-enrolment date
11-August-2024
Census date
31-August-2024
Last withdraw without fail date
13-September-2024
Results released date
03-December-2024

Learning outcomes

Students who successfully complete this unit will be able to:

  • Demonstrate an understanding of the roles of business analytics and artificial intelligence in various organisational contexts
  • Synthesise and develop appropriate business solution scenarios using appropriate machine learning, analytics and visualisation techniques
  • Analyse business problems and define the data requirements and business rules associated with the data-driven and machine learning problem-solving approaches
  • Demonstrate critical thinking and problem solving through statistical and machine learning computing and visualization
  • Communicate effectively as a professional and function as an effective leader or member of a team

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
On-campus
Class
2.00 12 weeks 24
Online
Directed Online Learning and Independent Learning
1.00 12 weeks 12
Unspecified Activities
Various
9.50 12 weeks 114
TOTAL150

Assessment

Type Task Weighting ULO's
Problem SolvingIndividual 20 - 30% 3,4 
ProjectGroup 30 - 50% 1,2,3,4,5 
ProjectIndividual 30 - 40% 2,3,4 

Content

  • The role and value of business analytics and artificial intelligence in business operations and strategic planning.
  • The difference between business rules and business data requirements.
  • Artificial intelligent applications as sources of business data
  • How Artificial Intelligence can support and enhance marketing analytics, financial analytics, sports analytics, geospatial analytics, security analytics, health analytics, Social Network Analysis (SNA). 
  • The use of Artificial Intelligence in data analysis visualisation, dashboard design, storyboarding 
  • Predictive, prescriptive, sentiment analytics, geospatial analytics
  • Fast data, data lake, social media data, open data

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.