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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

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
Prerequisites:
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(DATA2001 OR DATA2901) AND (DATA2002 OR DATA2902)
Corequisites:
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None
Prohibitions:
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None
Assumed knowledge:
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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 the relevant session.