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Unit outline_

DATA4207: Data Analysis in the Social Sciences

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

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
? 
132 credit points
Corequisites
? 
None
Prohibitions
? 
DATA5207
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Shaun Ratcliff, shaun.ratcliff@sydney.edu.au
The census date for this unit availability is 2 April 2024
Type Description Weight Due Length
Assignment group assignment Group work
Group work.
25% Multiple weeks n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment hurdle task Research project
Complete major research project.
50% STUVAC
Due date: 31 May 2024 at 23:59
2500 words.
Outcomes assessed: LO3 LO4 LO5 LO6 LO7
Assignment hurdle task Research plan
Develop plan for major research project.
20% Week 06
Due date: 29 Mar 2024 at 23:59
800 words.
Outcomes assessed: LO3 LO4 LO5 LO6 LO7
Tutorial quiz In class quiz
30 minute in class quiz.
5% Week 07 30 minutes
Outcomes assessed: LO5 LO7 LO6
hurdle task = hurdle task ?
group assignment = 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 (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

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 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.

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.

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

WK Topic Learning activity Learning outcomes
Week 01 Understanding the social world using data science Block teaching (3 hr)  
Week 02 Visualising social science data Block teaching (3 hr)  
Week 03 Confounding factors and human behaviour Block teaching (3 hr)  
Week 04 Model building Block teaching (3 hr)  
Week 05 Logistic regression and probability Block teaching (3 hr)  
Week 06 Predicting outcomes in the social world Block teaching (3 hr)  
Week 07 Developing your research plan (pre-recorded, online) Block teaching (3 hr)  
Week 08 Understanding human behaviour through survey design Block teaching (3 hr)  
Week 09 Measuring latent variables in the social world Block teaching (3 hr)  
Week 10 Using spatial data to understand the world Block teaching (3 hr)  
Week 11 Causality in the social world Block teaching (3 hr)  
Week 12 Data journalism Block teaching (3 hr)  
Week 13 Conclusion, and developing your research project Block teaching (3 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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.