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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_

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.


Academic unit Computer Science
Unit code DATA5207
Unit name Data Analysis in the Social Sciences
Session, year
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Assumed knowledge


Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Shaun Ratcliff,
Lecturer(s) Shaun Ratcliff ,
Type Description Weight Due Length
Assignment group assignment Group work
Due to COVID-19 this will be a take home solo project.
30% Multiple weeks n/a
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Tutorial quiz Class test 1
Due to COVID-19, this will be take home & open book.
5% Week 05 20 minutes
Outcomes assessed: LO5 LO6 LO7
Assignment Research plan
15% Week 06 600 words
Outcomes assessed: LO3 LO4 LO5 LO6 LO7
Tutorial quiz Class test 2
Due to COVID-19, this will be take home and open book.
5% Week 09 20 minutes
Outcomes assessed: LO5 LO6 LO7
Tutorial quiz Class test 3
Due to COVID-19, this will be take home and open book.
5% Week 13 20 minutes
Outcomes assessed: LO5 LO6 LO7
Assignment Research project
40% Week 14 (STUVAC) 2000 words
Outcomes assessed: LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?
  • In-class tests: These are short, 20 minute in-class tests designed to ensure students are progressing as expected. Each test will consist of three questions requiring written answers. Approximately half the material in each will explicitly cover social science applications rather than simply the methods involved, the other half a specifically methods-related question.
    Group work: These will be in-class group projects (of ~5 members), run over multiple weeks, to encourage peer-assisted learning. The group work will generally provide the opportunity to practice and learn the methods covered that week, as well as design the type of research project that comprises the final assessment of the unit. Students will work together to focus on different parts of the project, and quickly analyse data and write up their results. The finished product will be submitted for grading at the end of the seminar.
  • Research plan: Students will choose from four possible questions and several sets of data that will be provided early in the semester. Students will outline their approach to the question, literature that informs it (only five sources are required for the plan), and the methodology that the student intends to use to answer the question. Assessments will need to be completed in R Markdown.
  • Research project: The report will answer the question chosen for the research plan. Grades will be awarded for quality of analysis and presentation, and how well the methods and material covered in this class are used.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Understanding the social world using data science (3 hr) LO5 LO6 LO7
Week 02 Visualising social science data (3 hr) LO3 LO5 LO6 LO7
Week 03 Confounding factors and human behaviour (3 hr) LO3 LO5 LO6 LO7
Week 04 Understanding economic behaviour (3 hr) LO3 LO5 LO6 LO7
Week 05 Understanding the probability of real world problems (3 hr) LO3 LO5 LO6 LO7
Week 06 Understanding human behaviour through survey design (3 hr) LO2 LO7
Week 07 Causality in the social world, and using spatial data (3 hr) LO3 LO5 LO6 LO7
Week 08 Predicting outcomes in the social world (3 hr) LO1 LO2 LO3 LO5 LO6 LO7
Week 09 Non-linear problems in the social sciences (3 hr) LO2 LO3 LO5 LO6 LO7
Week 10 Measuring latent variables in the social world (3 hr) LO2 LO3 LO5 LO6 LO7
Week 11 Regularisation and variable selection in the social sciences (3 hr) LO2 LO3 LO5 LO6 LO7
Week 12 Quantitative social science in the wild (data journalism) (3 hr) LO2 LO3 LO5 LO6 LO7
Week 13 Conclusion, and developing your research project (3 hr) LO2 LO3 LO5 LO6 LO7

Attendance and class requirements

COVID-19 Announcement:

This unit will now be taught online. Zoom / Echo360 / Discussion forums through Edstem will replace regularly scheduled class time and / or consultations. Recordings will be made available to students and accessibility needs will be considered. 

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

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.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
This unit has been offered for a number of years and the UoS coordinators have continually worked to improve the quality of teaching materials, learning activities and forms of assessment based on student feedback.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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