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Unit outline_

SSPS4102: Data Analytics in the Social Sciences

Semester 1, 2022 [Normal day] - Remote

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

Academic unit Social and Political Sciences
Credit points 6
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
Prohibitions
? 
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
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: LO2
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: LO3 LO4
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: LO1

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 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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction and Overview Lecture and tutorial (3 hr) LO1
Week 02 Measuring and summarising data Lecture and tutorial (3 hr) LO1
Week 03 Data Visualisation Lecture and tutorial (3 hr) LO1 LO2
Week 04 Data collection and manipulation Lecture and tutorial (3 hr) LO1
Week 05 Bivariate association and causality Lecture and tutorial (3 hr) LO1
Week 06 Dimension Reduction Lecture and tutorial (3 hr) LO1
Week 07 Conducting Reproducible Research Lecture and tutorial (3 hr) LO1 LO3
Week 08 Normal Curve & Confidence intervals Lecture and tutorial (3 hr) LO1
Week 09 Hypothesis Testing Lecture and tutorial (3 hr) LO1
Week 10 Simple regression Lecture and tutorial (3 hr) LO1
Week 11 Multiple regression Lecture and tutorial (3 hr) LO1
Week 12 Logistic regression and advanced topics Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 13 Computational text analysis Lecture and tutorial (3 hr) LO1

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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.