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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

We are currently working to resolve an issue where some unit outline links are unavailable. If the link to your unit outline does not appear below, please use the link in your Canvas site. If no link is available on your Canvas site, please contact your unit coordinator.

Unit of study_

DATA3406: Human-in-the-Loop Data Analytics

This unit focuses on methods and techniques to take into consideration the human elements in data science. Humans can act as both sources of data and its interpreters, introducing a range of complexities with regards to analysis. How do we account for the unreliability in data collected from humans? What can be done to address the subjects' concerns about their data? How can we create visualisations that facilitate understanding of the main findings? What are the limitations of any predictions? The ability to consider human factors is essential in any loop that involves people gathering, storing, or interpreting data for decision making. On completion of this unit, students will be able to identify and analyse the human factors in the data analytics loop, and will be able to derive solutions for the challenges that arise.

Code DATA3406
Academic unit Computer Science
Credit points 6
(DATA2001 OR DATA2901) AND (DATA2002 OR DATA2902)
Assumed knowledge:
Basic statistics, database management, and programming.

At the completion of this unit, you should be able to:

  • LO1. communicate the process used to analyse a large data set, and to justify the methods used in the context of the humans gathering the data and interpreting the analysis
  • LO2. use interactive visualisation to communicate the thought process behind complex analytical questions.
  • LO3. communicate the results produced by an analysis pipeline, in oral and written form, including meaningful diagrams
  • LO4. identify ethical and legal issues that may relate to a data analytics task
  • LO5. understand the diverse roles of humans in the data analysis process
  • LO6. understanding the technical issues that are present when data is gathered from or used by humans
  • LO7. demonstrate use of appropriate technologies to address the technical issues of human-centred data analysis
  • LO8. carry out (in guided stages) the whole design and implementation cycle for creating a human-in-the-loop pipeline to analyse a dataset
  • LO9. identify explicit and implicit requirements for carrying out a data analysis task to address specific stakeholder purposes
  • LO10. select statistical techniques appropriate for modelling uncertainty and bias in data, and students can justify their choice
  • LO11. select appropriate techniques for validating their uncertain models, and ability to justify the choice.

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.

There are no unit outlines available online for previous years.