Overview

In this unit students will extend their quantitative and qualitative analytical capabilities and learn to apply them in a structured and methodological approach in order to offer critical insight to aviation industry problems. Using industry standard tools students will apply inferential statistical methods and research design principles to deliver objective and reliable aviation business insight.

Requisites

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
01-March-2027
30-May-2027
Last self-enrolment date
14-March-2027
Census date
30-March-2027
Last withdraw without fail date
20-April-2027
Results released date
06-July-2027

Unit learning outcomes

Students who successfully complete this unit will be able to:

  1. Apply inferential statistical techniques to aviation data to generate sophisticated understanding of practical business problems
  2. Confidently utilise industry standard advanced analytical tools
  3. Interface multiple tools, techniques, and data sources to create an analysis pipeline which addresses a specific industry problem
  4. Devise an appropriate research framework to address quantitative, qualitative or mixed applied aviation challenges
  5. Describe the challenges and opportunities related to emerging issues in data science and analytics in the aviation industry

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
On-Campus
Lecture		 2.00  12 weeks  24
On-Campus 
Class		 1.00  12 weeks  12
Unspecified Activities 
Independent Learning		 9.50  12 weeks  114
TOTAL     150

Assessment

Type Task Weighting ULO's
Assignment Individual  30 - 40%  1,2 
Assignment Individual/Group  30 - 40%  3,4 
Presentation Individual/Group  20 - 30%  1,4,5 
Laboratory Tutorial Quizzes Individual  10 - 20%  1,2,3,4,5 

Content

  • Introduction to Aviation Analytics
    • Analytics framework and applications in the aviation industry
    • Aviation data sources, databases and operational information systems
    • Emerging trends in aviation analytics
  • Descriptive and Diagnostic Analytics
    • Data preparation, cleaning and exploration
    • Descriptive statistics and data interpretation
    • Correlation analysis and diagnostic techniques
    • Application of industry-standard analytical and statistical tools 
  • Data Visualisation and Business Intelligence
    • Data modelling and transformation
    • Dashboard development and business intelligence
    • Principles of effective data visualisation and managerial communication
  • Predictive Analytics
    • Regression modelling and interpretation
    • Forecasting and predictive decision support
    • Introduction to qualitative research methods for aviation studies
  • Prescriptive Analytics and Decision Support
    • Formulating aviation business problems as optimisation models
    • Linear programming and optimisation techniques
    • Sensitivity analysis and decision evaluation
  • Applied Aviation Research
    • Integrating multiple analytical tools, operational systems and data sources
    • Analysing operational and/or business data to support decision-making
    • Communicating analytical findings through reports, dashboards and presentations
    • Applying quantitative and qualitative research methods to solve aviation industry problems

Study resources

Reading materials

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