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Unit of study_
DATA5207: Data Analysis in the Social Sciences
Data science is a new, rapidly expanding field. There is an unprecedented demand from technology companies, financial services, government and not-for-profits for graduates who can effectively analyse data. This subject will help students gain a critical understanding of the strengths and weaknesses of quantitative research, and acquire practical skills using different methods and tools to answer relevant social science questions. This subject will offer a nuanced combination of real-world applications to data science methodology, bringing an awareness of how to solve actual social problems to the Master of Data Science. We cover topics including elections, criminology, economics and the media. You will clean, process, model and make meaningful visualisations using data from these fields, and test hypotheses to draw inferences about the social world. Techniques covered range from descriptive statistics and linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks, to the analysis of unstructured text as data, multilevel and geospatial modelling, all using the open source program R. In doing this, not only will we build on the skills you have already mastered through this degree, but explore different ways to use them once you graduate.
| Code | DATA5207 |
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| Academic unit | Computer Science |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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DATA4207 |
At the completion of this unit, you should be able to:
- LO1. demonstrate familiarity with the various ethical issues and professional standards around the gathering of data
- LO2. demonstrate proficiency in the delivery of a small-scale project, and the management of the project from initial conception to delivery to evaluation
- LO3. present data and reports of a high standard
- LO4. autonomously collect, collate, assess and compare data from multiple sources, such as the Australian Bureau of Statistics and the Australian Data Archive. You will be able to discern the quality of data to a minute level, and be able to draw a broad range of insights from data of various degrees of statistical significance
- LO5. apply established data analytical methodology in a sophisticated manner and have a medium degree of proficiency in methodological procedures to approach complex problems specifically related to the social sciences
- LO6. utilise industry-leading concepts and frameworks in your pedagogy and direct formidable amounts of data for protracted, complex insights into areas such as polling data and demography
- LO7. apply a theoretical understanding of statistical methods to practical problems around data gathering methodology, statistical significance and sample sizing, and autonomously create basic design frameworks for statistical modelling problems.
Unit outlines
Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.
- Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2023 [Normal day] - Remote
- Intensive December, 2023 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney
- Intensive December, 2024 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2020 [Normal evening] - Camperdown/Darlington, Sydney
- Semester 1, 2021 [Normal day] - Remote
- Intensive February, 2021 [Normal day] - Remote
- Intensive December, 2021 [Normal day] - Remote
- Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2022 [Normal day] - Remote
- Intensive December, 2022 [Normal day] - Camperdown/Darlington, Sydney
- Intensive December, 2022 [Normal day] - Remote
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