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Unit of study_
DATA4207: Data Analysis in the Social Sciences
2025 unit information
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.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Undergraduate |
|---|---|
| Academic unit | Computer Science |
| Credit points | 6 |
|
Prerequisites:
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DATA3888 or COMP3888 or COMP3988 or CSEC3888 or SOFT3888 or ENGG3112 or SCPU3001 |
|---|---|
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Corequisites:
?
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None |
| Prohibitions:
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DATA5207 |
| Assumed knowledge:
?
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A major in a computer science area |
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 using RStudio
- 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 availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2024
|
Normal day | Camperdown/Darlington, Sydney | |
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Intensive November - December 2024
|
Normal day | Camperdown/Darlington, Sydney |
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
|
Semester 1 2025
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
|
|
Intensive December 2025
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
|
Find your current year census dates
Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
Important enrolment information
Departmental permission requirements
If you see the ‘Departmental Permission’ tag below a session, it means you need faculty or school approval to enrol. This may be because it’s an advanced unit, clinical placement, offshore unit, internship or there are limited places available.
You will be prompted to apply for departmental permission when you select this unit in Sydney Student.
Read our information on departmental permission.
Additional advice
The Intensive December offering of this unit requires departmental permission to ensure appropriate foundational knowledge is met, given the steep learning curve. Students must have achieved a WAM of at least 65% to be considered.
