This unit aims to develop students’ conceptual and practical understanding of the field of data analytics in the contexts of real-world applications. The students will learn about basic concepts, techniques and popular tools in various aspects of data analytics, following the lifecycle of a practical data analytics project which involves data collection, wrangling, analytics and visualisation.


Teaching Periods
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:

  • Exhibit advanced data analysis processes using Python programming
  • Articulate relevant data analytics use cases using different Python libraries
  • Present exploratory visualisations using Python

Teaching methods

Swinburne Online

Type Hours per week Number of weeks Total (number of hours)
Directed Online Learning and Independent Learning
15.00 10 weeks 150


Type Hours per week Number of weeks Total (number of hours)
Online Contact (Phasing out)
Online Class
1.50 8 weeks 12
Directed Online Learning and Independent Learning
1.50 8 weeks 12
Face to Face Contact (Phasing out)
3.00 8 weeks 24
Unspecified Learning Activities (Phasing out)
Independent Learning
12.75 8 weeks 102


Type Task Weighting ULO's
Assignment 1Individual/Group 40 - 60% 1,2 
Assignment 2Individual 40 - 60% 1,2,3 


  • Introduction to advanced analytics
  • Introduction to Python Programming
  • Data ingestion and wrangling
  • SQL for Data Analysis
  • Data Visualisation using Matplotlib
  • Data Visualisation using Seaborn
  • Improving the Visualisations in Python
  • Dashboard and Interactive Visualisations

Study resources

Reading materials

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