Outline

Overview

Social science research abounds with data of different nature, sources, and forms. In many situations, available data can be summarised and analysed quantitatively to identify trends, uncover patterns, or test hypotheses. This unit presents fundamental ideas behind modern data analytics and provides students with hands-on experience of analysing social science data. First, we cover data manipulation, data cleaning, and basic visualisation. We will then move on to the methods of exploratory data analysis and finding patterns in data. The unit will conclude with an introduction to statistical inference, linear regression analysis and its limitations for causal analysis.

Unit details and rules

Academic unit	Government and International Relations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Francesco Bailo, francesco.bailo@sydney.edu.au
Lecturer(s)	Francesco Bailo, francesco.bailo@sydney.edu.au

Assessment

The census date for this unit availability is 2 September 2024

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Research project Design and conduct a data analysis project addressing research question(s)	65%	Formal exam period Due date: 11 Nov 2024 at 23:59	3000wd
Outcomes assessed:
Attendance	Multiple choice quizzes Multiple choice quizzes on week topics and key concepts	5%	Multiple weeks	5 x 50wd equivalent
Outcomes assessed:
Assignment	Research proposal Write a research proposal for the final research project	15%	Week 04 Due date: 23 Aug 2024 at 23:59	700wd
Outcomes assessed:
Assignment	Data analysis exercise Collect, clean, manipulate, describe, and analyse data, report results	15%	Week 09 Due date: 27 Sep 2024 at 23:59	550wd
Outcomes assessed:

Assessment summary

Students will first complete multiple-choice quizzes throughout the semester. These quizzes are designed to reinforce key concepts and assess ongoing understanding. A research proposal for the final project requires students to outline their intended research, including objectives, data and methodology. In a data analysis exercise, students will collect, clean, manipulate, describe, and analyse data, subsequently reporting their findings in a concise report. The most significant component is the research project. This extensive project will allow students to demonstrate their ability to conduct quantitative research, apply analytical techniques, and present their results comprehensively.

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

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 sydney.edu.au/students/guide-to-grades

For more information see guide to grades.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard late penalties apply.

Academic integrity

The Current Student website provides information on academic integrity 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 integrity breaches seriously.

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

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 2023 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 2023. 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	Overview and Introduction to R	Seminar (3 hr)
Week 02	Causality	Seminar (3 hr)
Week 03	Data Visualisation with R	Seminar (3 hr)
Week 04	Measurement	Seminar (3 hr)
Week 05	Data Transformation and Communication with R	Seminar (3 hr)
Week 06	Prediction	Seminar (3 hr)
Week 07	Probability	Seminar (3 hr)
Week 08	Uncertainty	Seminar (3 hr)
Week 09	Machine Learning	Seminar (3 hr)
Week 10	Textual Data: Natural Language Features	Seminar (3 hr)
Week 11	Textual Data: Machine Learning Methods	Seminar (3 hr)
Week 12	Network and Spatial data	Seminar (3 hr)
Week 13	Ethical Considerations and Future Trends	Seminar (3 hr)

Attendance and class requirements

Students are expected to attend a minimum of 90 per cent of timetabled activities for a unit of study, unless granted exemption by the Associate Dean or relevant delegated authority. The Associate Dean or relevant delegated authority may determine that a student fails a unit of study because of inadequate attendance.

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

Readings can also be accessed on the eReserved List on Canvas.

Week	Topic	Required	Extra/Reference
01	Overview and Introduction to R	DASS Ch1 or QSS Ch1	HOPR Ch1, Ch2, Ch3, Ch5, Ch6, Ch7 & Ch8
02	Causality	DASS Ch2 or QSS Ch2
03	Data visualisation with R	R4DS Ch3	FA Ch1 & Ch2 & TA Ch2
04	Measurement	DASS Ch3 or QSS Ch3
05	Data transformation and communication with R	R4DS Ch5, Ch18 & Ch27	RMD Ch1, Ch2 & Ch3
06	Prediction	DASS Ch4 or QSS Ch4
07	Probability	DASS Ch6 or QSS Ch6
08	Uncertainty	DASS Ch7 or QSS Ch7
09	Machine Learning	MLwR Ch1, Ch3 & Ch6	I2SL Ch4
10	Textual Data: Natural Language Features	SML4TA Part1 (Ch1, Ch2, Ch3, Ch4 & Ch5)
11	Textual Data: Machine Learning Methods	SML4TA Part 2 (Overview, Ch6 & Ch7)
12	Network and Spatial data	I2RDS Ch7 & SS4DS Ch2 & Ch3
13	Ethical Considerations and Future Trends	Blanchard & Taddeo (2023) & Fuller (2023) & Timans et al. (2019)	boyd & Crawford (2012) & Floridi (2012)

Textbooks

NOTE: You do not need to purchase both textbooks (you should get the one you prefer). DASS is a more friendly and accessible version of QSS. Both textbooks will introduce you to statistical concepts using R.

Llaudet, E., & Imai, K. (2022). Data analysis for social science: A friendly and practical introduction. Princeton University Press. https://press.princeton.edu/books/hardcover/9780691199429/data-analysis-for-social-science (DASS)
- Publicly and fully available online: NO
- Online access through the library: NO
- Copy available through library: YES (but only one copy!) link
Imai, K. (2017). Quantitative social science: An introduction. Princeton University Press. https://press.princeton.edu/books/paperback/9780691175461/quantitative-social-science (QSS)
- Publicly and fully available online: NO
- Library access: NO
- Copy available through library: YES (but only one copy and the tidyverse version!) link

Other texts

Blanchard, A., & Taddeo, M. (2023). The ethics of artificial intelligence for intelligence analysis: A review of the key challenges with recommendations. Digital Society, 2(1), 12. https://doi.org/10.1007/s44206-023-00036-4
- Publicly and fully available online: YES link
boyd, d. & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
- Publicly and fully available online: NO
- Online access through library: YES link
Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization. New Riders. https://learning.oreilly.com/library/view/the-functional-art/9780133041187/ (FA)
- Publicly and fully available online: NO
- Library access: YES link
  - Online access: YES link
  - Hard Copy: YES link
Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication. Pearson Education. https://learning.oreilly.com/library/view/the-truthful-art/9780133440492 (TA)
- Publicly and fully available online: NO
- Library access: YES link
  - Online access: YES link
  - Hard Copy: NO
Floridi, L. (2012). Big data and their epistemological challenge. Philosophy & Technology, 25(4), 435–437. https://doi.org/10.1007/s13347-012-0093-4
- Publicly and fully available online: YES link
Fuller, S. (2023, dicembre 18). Is data surfacing the future of empirical social research? Impact of Social Sciences. https://blogs.lse.ac.uk/impactofsocialsciences/2023/12/18/is-data-surfacing-the-future-of-empirical-social-research/
- Publicly and fully available online: YES link
Grolemund, G. (2014). Hands-on programming with R. O’Reilly. https://rstudio-education.github.io/hopr/ (HOPR)
- Publicly and fully available online: YES link
Holster, J. D. (2022). Introduction to R for data science: A LISA 2020 guidebook. https://bookdown.org/jdholster1/idsr/
- Publicly and fully available online: YES link
Hvitfeldt, E., & Silge, J. (2021). Supervised machine learning for text analysis in R. Chapman and Hall/CRC. https://doi.org/10.1201/9781003093459 (SML4TA)
- Publicly and fully available online: YES link
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R. Springer. https://www.statlearning.com/ (I2SL)
- Publicly and fully available online: YES link
Lantz, B. (2023). Machine learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data. Packt Publishing. https://www.packtpub.com/en-us/product/machine-learning-with-r-9781801071321 (MLwR)
- Publicly and fully available online: NO
- Library access: YES link
  - Online access: YES link
  - Hard Copy: NO
Moraga, P. (2023). Spatial statistics for data science: Theory and practice with R. Chapman and Hall/CRC. https://doi.org/10.1201/9781032641522 (SS4DS)
- Publicly and fully available online: YES link
Timans, R., Wouters, P., & Heilbron, J. (2019). Mixed methods research: What it is and what it could be. Theory and Society, 48(2), 193–216. https://doi.org/10.1007/s11186-019-09345-5
- Publicly and fully available online: YES link
Xie, Y., Allaire, J. J., & Grolemund, G. (2019). R Markdown: The definitive guide. CRC Press. https://bookdown.org/yihui/rmarkdown/ (RMD)
- Publicly and fully available online: YES link
Wickham, H., & Grolemund, G. (2017). R for data science. O’Reilly. https://r4ds.had.co.nz/ (R4DS)
- Publicly and fully available online: YES link

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 and reports.
LO3. Demonstrate understanding of statistical concepts to address social science research problems.
LO4. Use professional tools for data analysis such as R and RStudio.
LO5. Demonstrate understanding of how to use data and data analytics in social research design.

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 is a new unit

Additional information

Use of Generative AI and other marvels

The use of generative artificial intelligence (GAI) tools such as GPT-3, LaMDA, LLaMA, BLOOM, GPT-4 and Gemini is permitted, but their output must not be included in your submission. You can use GAI to get help to

use software,
summarise, structure or restructure texts,
identify relevant ideas, works, authors, data and methods,
brainstorm ideas.

The use of writing assistance tools such as grammar checkers, translation and paraphrasing tools, and reference generators is permitted, and their output can be included in the text you submit.

If you use any of these tools in preparing your assignment, you must acknowledge it in your submission, indicating what tools you have used and how you have used them.

Independent of the software tools you use, you are solely responsible for what you submit, and you must comply with the university's policies.

Please refer to this page https://www.sydney.edu.au/students/academic-integrity/new-policy.html for details on the Academic integrity policy.

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

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

SSPS6006: Data Analytics for Social Research

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect