Outline

Overview

This unit of study introduces social science students to statistical concepts and quantitative methods for different types of data. It equips students with practical programming skills for social science research. It introduces some key techniques for presenting, communicating, and analysing data, including data visualisation and pattern discovery.

Unit details and rules

Academic unit	Social and Political Sciences
Credit points	6
Prerequisites ?	144 credit points and (FASS3999 or FASS3333 or equivalent)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Successful completion of a Table A major from the Faculty of Arts and Social Sciences
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Francesco Bailo, francesco.bailo@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Practical skill	Data preparation and communication Data preparation and communication task	30%	Week 06 Due date: 03 Apr 2026 at 23:59	1,000 words	AI allowed
Outcomes assessed:
Written work	Data analysis project Written report on data analysis project	40%	Week 13 Due date: 29 May 2026 at 23:59	1,500 words	AI allowed
Outcomes assessed:
In-class quiz	In-class interactive R programming exercise and quiz 10 weekly coding exercises with brief written interpretation of R output	10%	Weekly	10 × 100 words equivalent	AI allowed
Outcomes assessed:
Out-of-class quiz	Out-of-class interactive R programming exercise and quiz 10 weekly coding exercises with brief written interpretation of R output	20%	Weekly	10 × 100 words equivalent	AI allowed
Outcomes assessed:
= hurdle task ?

Assessment summary

The assessment structure for this unit has been designed to support continuous, hands‑on practice in R programming and to better reflect authentic data analysis workflows. Instead of a small number of large written tasks, you will engage in regular formative activities that build skills progressively throughout the semester.

1. In‑Class Interactive R Exercises and Quizzes: 10%

Across the 13 teaching weeks, you will complete 10 short, interactive exercises and quizzes during class. These are designed to reinforce core concepts, provide immediate feedback, and help you practise applying methods in R in a supportive environment.

2. Out‑of‑Class R Exercises and Quizzes: 20%

You will also complete 10 short out‑of‑class exercises. These allow you to consolidate your understanding beyond class time and develop confidence working independently. Regular, low‑stakes practice is a key part of mastering R.

3. Data Preparation and Communication Task (1,000 words): 30%

With this task you will demonstrate your ability to prepare, clean, and document a dataset, and to communicate your decisions clearly and effectively. The task emphasises reproducibility, clarity, and sound data‑handling practices.

4. Final Data Analysis Project (1,500 words): 40%

The final analysis gives you the opportunity to apply the full set of skills developed across the semester. You will conduct an end‑to‑end analysis (from research question to interpretation) and present your findings in a clear, analytically rigorous format.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (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

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Fail

0 - 49

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

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	Introduction to R & Data Science Workflow	Workshop (3 hr)
Week 02	Reproducible Workflows and Version Control	Workshop (3 hr)
Week 03	Data Acquisition and Measurement	Workshop (3 hr)
Week 04	Data Visualization	Workshop (3 hr)
Week 05	Data Cleaning and Probability Simulation	Workshop (3 hr)
Week 06	Simple Linear Regression	Workshop (3 hr)
Week 07	Multiple Regression	Workshop (3 hr)
Week 08	Model Diagnostics and Communication	Workshop (3 hr)
Week 09	Logistic Regression	Workshop (3 hr)
Week 10	Count Models and Multilevel Modeling	Workshop (3 hr)
Week 11	Surveys and Experimental Design	Workshop (3 hr)
Week 12	Causal Inference from Observational Data	Workshop (3 hr)
Week 13	Advanced Applications and Best Practices	Workshop (3 hr)

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

Weekly readings are indicated under the Reading List tab on the unit’s Canvas site.

This unit adopts two core textbooks, each serving a complementary role in developing your skills in data analysis and statistical reasoning:

1. Alexander, R. (2023). Telling stories with data: With applications in R. CRC Press.
We will be reading significant parts of this book throughout the semester. It offers an accessible and applied introduction to data analysis using R. A freely available online version can be accessed on the author’s website:
https://rohanalexander.github.io/telling_stories-published/

2. Gelman, A., Hill, J., & Vehtari, A. (2021). Regression and Other Stories. Cambridge University Press.
We will also cover substantial portions of this more technical text. While it is denser and may feel less accessible than Telling Stories with Data, you are not expected to master every detail. The goal is to help you become familiar with the technical language and conventions common in academic and statistical literature. Please engage with the assigned chapters as best you can. An online version is available through the University of Sydney Library:
https://www.cambridge.org/highereducation/books/regression-and-other-stories/DD20DD6C9057118581076E54E40C372C

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. Find, clean, and analyse data from diverse sources.
LO2. Produce meaningful data visualisations.
LO3. Demonstrate understanding of how data analysis enables social scientists to address social science problems.
LO4. Demonstrate understanding of how new data sources have expanded the power of social sciences.

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.

Feedback from 2021–2025 has played a central role in shaping the unit’s teaching approach and assessment design, particularly to better support students with limited prior experience in quantitative analysis.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

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

SSPS4102: Data Analytics in the Social Sciences

Semester 1, 2026 [Normal day] - 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

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