Overview

Students will develop and enhance their conceptual and practical understanding in data analytics in a 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 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.

Requisites

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date

Learning outcomes

Students who successfully complete this unit will be able to:

  • Demonstrate an understanding of the roles of business analytics in various organisational contexts
  • Synthesise and develop appropriate business solution scenarios using appropriate analytics and visualisation techniques
  • Analyse business problems and define the data requirements and business rules associated with the data-driven problem-solving approaches
  • Demonstrate critical thinking and problem solving through statistical 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)
Face to Face Contact (Phasing out)
Tutorial Labs		2.00 12 weeks 24
Online
Directed Online Learning and Independent Learning		1.00 12 weeks 12
Unspecified Learning Activities (Phasing out)
Independent Learning		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

  • Use of and value of analytics in Business divisions, management functions, different management levels, performance management
  • Business rules vs business data requirements
  • Marketing analytics, financial analytics, sports analytics, geospatial analytics, security analytics, health analytics, Social Network Analysis (SNA), etc
  • Multi-platform analytics (mobile, cloud, Enterprise Resource Planning (ERP))
  • Data visualisation, dashboard design, storyboarding with analytics
  • Data-driven decision making vs Intuitive decision making
  • Introduction to advanced analytics: Predictive, Prescriptive, sentiment analytics, geospatial analytics
  • Forecasting and predictive analytics, scenario planning
  • 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.