SSPS4102: Semester 1, 2022

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Units SSPS4102 Semester 1 2022 [Normal day]

Unit of study_

SSPS4102: Data Analytics in the Social Sciences

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

Our lives are increasingly shaped by data, not only from official government agencies but from diverse sources in our social world. As the sources, form and content of data diversify somust the social science techniques for analysing and using that data. This unit of study focuses on the world of big data. It equips students with an understanding of how the emergence of big data has expanded the power and scope of the social sciences and of how to make use of big data for social science purposes. It introduces some key techniques for presenting, communicating, and analysing data, including data visualisation and pattern discovery.

Unit details and rules

Unit code	SSPS4102
Academic unit	Social and Political Sciences
Credit points	6
Prohibitions ?	None
Prerequisites ?	ANTH3998 or ANTH3999 or CRIM3998 or CRIM3999 or ECOP3998 or ECOP3999 or GOVT3898 or GOVT3900 or SCLG3998 or SCLG3999 or SLSS3998 or SLSS3999
Corequisites ?	None
Assumed knowledge ?	36 credits points towards a major in a relevant subject area in the social sciences or humanities
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Dongju Lee, dongju.lee@sydney.edu.au
Lecturer(s)	Dongju Lee, dongju.lee@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Data visualisation and communication exercise Import and clean data, make relevant plots, describe results	40%	Week 07 Due date: 08 Apr 2021 at 23:59	1500wd
Outcomes assessed:
Assignment	Short essay Reflect on social, political, and/or ethical challenges of big data	20%	Week 10 Due date: 06 May 2022 at 23:59	1000wd
Outcomes assessed:
Assignment	Big data analysis exercise Collect, clean, manipulate, describe, and analyse data, report results	40%	Week 13 Due date: 29 May 2022 at 23:59	2000wd
Outcomes assessed:

Assessment summary

Assessment consists of a short essay that provides on opportunity to reflect on some of the social, political, and/or ethical challenges big data pose, and two data analysis exercises. These data analysis exercises build on the in-class tutorials and create an opportunity for students to apply their new analytical skills to answer a substantive social science question using data.

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.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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	Introduction and Overview	Lecture and tutorial (3 hr)
Week 02	Measuring and summarising data	Lecture and tutorial (3 hr)
Week 03	Data Visualisation	Lecture and tutorial (3 hr)
Week 04	Data collection and manipulation	Lecture and tutorial (3 hr)
Week 05	Bivariate association and causality	Lecture and tutorial (3 hr)
Week 06	Dimension Reduction	Lecture and tutorial (3 hr)
Week 07	Conducting Reproducible Research	Lecture and tutorial (3 hr)
Week 08	Normal Curve & Confidence intervals	Lecture and tutorial (3 hr)
Week 09	Hypothesis Testing	Lecture and tutorial (3 hr)
Week 10	Simple regression	Lecture and tutorial (3 hr)
Week 11	Multiple regression	Lecture and tutorial (3 hr)
Week 12	Logistic regression and advanced topics	Lecture and tutorial (3 hr)
Week 13	Computational text analysis	Lecture and tutorial (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

All the readings can be accessed on the eReserved List on Canvas.

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 has assisted to shape the teaching and required tasks in this unit in 2022, particularly to accommodate students who do not have previous quantitative analysis skills.

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

Disclaimer

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