Outline

Overview

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

Academic unit	Computer Science
Credit points	6
Prerequisites ?	DATA3888 or COMP3888 or COMP3988 or CSEC3888 or ISYS3888 or SOFT3888 or ENGG3112 or SCPU3001
Corequisites ?	None
Prohibitions ?	DATA5207
Assumed knowledge ?	A major in a computer science area
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Kathryn Robison, kathryn.robison@sydney.edu.au

Assessment

The census date for this unit availability is 5 December 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Research project Complete major research project	50%	Formal exam period Due date: 29 Dec 2025 at 23:59	2500 words	AI allowed
Outcomes assessed:
Practical skill	Group work Group work	25%	Multiple weeks	n/a	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	In class quiz 30 minute in class quiz	5%	Week 02	30 minutes	AI prohibited
Outcomes assessed:
Research analysis	Research plan Develop plan for major research project	20%	Week 02 Due date: 12 Dec 2025 at 23:59	800 words	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

In-class tests: These are short, 30-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 ~4-5 members), deisgned to encourage teamwork 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.
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 (10 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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	• Understanding the social world using data science • Visualising social science data • Human behaviour studies and their errors • Surveys and probabilities • Predicting outcomes in the social world • Writing about social science data (I)	Lecture (6 hr)
Week 01	Tutorials will focus on applying what was learned in lecture to practical applications.	Tutorial (12 hr)
Week 02	Tutorials will focus on applying what was learned in lecture to practical applications.	Tutorial (12 hr)
Week 03	• Measuring latent variables in the social world • Validating social science data • Using spatial data to understand the world • Writing about social science data (II) • Data journalism • Communication analyses • Conclusion + developing a research project	Lecture (6 hr)
Week 03	Tutorials will focus on applying what was learned in lecture to practical applications.	Tutorial (12 hr)
Week 04	Tutorials will focus on applying what was learned in lecture to practical applications.	Tutorial (12 hr)

Attendance and class requirements

Attendance is compulsory, and will be marked.

COVID-19 Announcement:

This unit will be taught as a combined face-to-face and online class.

You will have the option to enrol in either online or in-person lectures and labs. Online lectures and labs will all be run live over Zoom.

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.

Required readings

These will be available through the unit Canvas page.

Learning outcomes

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.

Outcomes
Graduate qualities

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. 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
LO3. apply established data analytical methodology in a sophisticated manner using R and have a medium degree of proficiency in methodological procedures to approach complex problems specifically related to the social sciences
LO4. 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.
LO5. 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
LO6. demonstrate proficiency in the delivery of a small-scale project, and the management of the project from initial conception to delivery to evaluation
LO7. present data and reports of a high standard using RStudio

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
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

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.

Disclaimer

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

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

DATA4207: Data Analysis in the Social Sciences

Intensive November - December, 2025 [Block mode] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links