QBUS6860: Semester 2, 2021

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Units QBUS6860 Semester 2 2021 [Normal day]

Unit of study_

QBUS6860: Visual Data Analytics

Overview

Accurate and effective analysis of data is a crucial skill in today's data-rich business environment. Visual Data Analytics (VDA) is an indispensable scientific tool for analysing all sorts of business-related data and, in particular, complex high-dimensional data. Applications include the visualisation of financial statements, capital market data, marketing data, supply chain data and many others. VDA has the ability to encode vast amounts of information into a small space that can be then intuitively interpreted for decision-making. This unit draws upon statistics, computer science, behavioural psychology and information design for visualising numerical and text data. It presents statistical and data analysis methods that are necessary for description, exploration, inference and diagnosis using data reduction, visual mining, smoothing, clustering and validation techniques. Upon completion of the unit, students should be proficient in producing high integrity visuals that enable fast and precise business decision-making. Students will also learn about the limitations of visual perception and how to design powerful visuals that can tap into our natural cognitive predisposition in favouring visual types of information.

Details

Academic unit	Business Analytics
Unit code	QBUS6860
Unit name	Visual Data Analytics
Session, year ?	Semester 2, 2021
Attendance mode	Normal day
Location	Camperdown/Darlington, Sydney
Credit points	6

Enrolment rules

Prohibitions ?	None
Prerequisites ?	QBUS5001 or QBUS5002
Corequisites ?	None
Assumed knowledge ?	The unit assumes knowledge of statistics and confidence in working with data.
Available to study abroad and exchange students	Yes

Teaching staff and contact details

Coordinator	Ganna Pogrebna, ganna.pogrebna@sydney.edu.au

Type	Description	Weight	Due	Length
Small continuous assessment	Weekly Participation Assignments N/A	40%	Multiple weeks	Varied
Outcomes assessed:
Assignment	Individual Assignment Individual assignment (provided at the start of the start of the semester)	60%	Week 13	5000
Outcomes assessed:

Weekly assignments: There are 8 regular assignments worth 5 marks each.
Individual assignment: There is an individual assignment, which you need to submit by the end of Week 13. It is worth 60 marks. IMPORTANT: You must use Python for all visualisations you produce in your individual assignment.

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.

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:

Late assignments will not be accepted unless special consideration is obtained.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Visual Data Analytics	Lecture (2 hr)
Week 01	Tutorial activity	Tutorial (1 hr)
Week 02	Data Types and Visualisation Types	Lecture (2 hr)
Week 02	Tutorial activity	Tutorial (1 hr)
Week 03	Exploratory Data Analysis using Visualisation	Lecture (2 hr)
Week 03	Tutorial activity	Tutorial (1 hr)
Week 04	Avoiding Biases and Dark Patterns	Lecture (2 hr)
Week 04	Tutorial activity	Tutorial (1 hr)
Week 05	Storytelling with Data Visualisation	Lecture (2 hr)
Week 05	Tutorial activity	Tutorial (1 hr)
Week 06	Interactive Visualisation and Human Data Interaction	Lecture (2 hr)
Week 06	Tutorial activity	Tutorial (1 hr)
Week 07	Visualisations for Decision-making	Lecture (2 hr)
Week 07	Tutorial activity	Tutorial (1 hr)
Week 09	Visualising Networks	Lecture (2 hr)
Week 09	Tutorial activity	Tutorial (1 hr)
Week 10	Visualising Processes	Lecture (2 hr)
Week 10	Tutorial activity	Tutorial (1 hr)
Week 11	Visualising Time-Series	Lecture (2 hr)
Week 11	Tutorial activity	Tutorial (1 hr)
Week 12	Visualising with Machine Learning	Lecture (2 hr)
Week 12	Tutorial activity	Tutorial (1 hr)
Week 13	Future of Data Visualisation	Lecture (2 hr)
Week 13	Tutorial activity	Tutorial (1 hr)

Attendance and class requirements

Lecture recordings: All lectures are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

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 study materials will be provided via the Canvas platform. Please, note that in Semester 2, 2021 this unit will use Python as the main platform. It is your responsibility to make sure that you have all requires software and packages for this unit installed on your computer prior to the semester start. You will receive installation instructions prior to the first lecture.

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. Explore information using graphical methods.
LO2. Match available data to the most appropriate visualisations to assist in problem solving.
LO3. Use visualisation appropriately and effectively to support business decision making and business problem solving.
LO4. Communicate your visual analytics results and explain your findings to a business audience.
LO5. Critically evaluate visualisation methods, through individual and stimulating work with peers.
LO6. Identify potential biases, which visualisations may generate, using developed experience in ethical and socially responsible visual analytics.

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

Closing the loop

Note that major changes have been made to this unit in Semester 2, 2021. This unit will use Python as the main platform.

Additional information

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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

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Additional information

Disclaimer

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