Useful links
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:
?
|
(DATA2001 OR DATA2901) AND (DATA2002 OR DATA2902) |
|---|---|
|
Corequisites:
?
|
None |
| Prohibitions:
?
|
None |
| Assumed knowledge:
?
|
Basic statistics, database management, and programming |
At the completion of this unit, you should be able to:
- LO1. Identify ethical and legal issues in a data analytics task to answer the driving questions for the analysis.
- LO2. Understand the diverse roles of people play in the full data analysis process and their implications for the ethical concerns and analysis methods.
- LO3. Identify explicit and implicit requirements for carrying out a data analysis task to account for the perspectives of different stakeholders.
- LO4. Understanding the particular challenges for data analysis when data is gathered from people.
- LO5. Select analytic and statistical techniques appropriate for modelling uncertainty and bias in data, and students can justify their choice
- LO6. Carry out (in guided stages) the whole design and implementation cycle for creating a human-in-the-loop pipeline to analyse a dataset to address a set of driving questions.
- LO7. Use Literate Programming to communicate the thought process behind the data analysis process.
- LO8. 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
- LO9. Communicate the results produced by an analysis pipeline, in oral and written form, accounting for the audience, making effective use of text, tables and visualisations.
- LO10. Select appropriate techniques for evaluating the effectiveness of information reported to stakeholders, and ability to analyse and report the results and the choice of methods.
Unit outlines
Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.
- Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney
- Key dates
Key dates through the academic year, including teaching periods, census, payment deadlines and exams.
- Student administration
Enrolment, course planning, fees, graduation, support services, student IT
- Expectations of student conduct
Code of Conduct for Students, Conditions of Enrollment, University Privacy Statement, Academic Integrity
- Academic appeals
Academic appeals process, special consideration, rules and guidelines, advice and support
- Learning and teaching policy
Policy register, policy search
- Financial support
Scholarships, interest free loans, bursaries, money management
- Study resources
Learning Centre, faculty and school programs, Library, online resources
- Health and support
Student Centre, counselling & psychological services, University Health Service, general health and wellbeing
