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 ?	None
Corequisites ?	None
Prohibitions ?	DATA4207
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Shaun Ratcliff, shaun.ratcliff@sydney.edu.au
Lecturer(s)	Shaun Ratcliff, shaun.ratcliff@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Group work Group work.	25%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Research project Complete major research project.	50%	STUVAC Due date: 02 Jun 2023 at 23:59	2500 words
Outcomes assessed:
Tutorial quiz	Class test 3 In class quiz.	5%	Week 06	30 minutes
Outcomes assessed:
Assignment	Research plan Develop plan for major research project.	20%	Week 09 Due date: 28 Apr 2023 at 23:59	800 words
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

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 ~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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Understanding the social world using data science	Lecture and tutorial (3 hr)
Week 02	Visualising social science data	Lecture and tutorial (3 hr)
Week 03	Confounding factors and human behaviour	Lecture and tutorial (3 hr)
Week 04	Model building	Lecture and tutorial (3 hr)
Week 05	Logistic regression and probability	Lecture and tutorial (3 hr)
Week 06	Predicting outcomes in the social world	Lecture and tutorial (3 hr)
Week 07	Understanding human behaviour through survey design	Lecture and tutorial (3 hr)
Week 08	Measuring latent variables in the social world	Lecture and tutorial (3 hr)
Week 09	Developing your research plan	Lecture and tutorial (3 hr)
Week 10	Using spatial data to understand the world	Lecture and tutorial (3 hr)
Week 11	Causality in the social world	Lecture and tutorial (3 hr)
Week 12	Data journalism	Lecture and tutorial (3 hr)
Week 13	Conclusion, and developing your research project	Lecture and tutorial (3 hr)

Attendance and class requirements

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. 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
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.

Additional information

Canvas

Details about weekly topics and assessments will be available on the unit Canvas page.

Late submissions

University policies on late submissions apply.

Academic Dishonesty and Plagiarism

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Computer programming assignments may also be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/) . These programs work in a similar way to TII in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

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

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

DATA5207: Data Analysis in the Social Sciences

Semester 1, 2023 [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

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