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Unit of study_

QBUS6860: Visual Data Analytics

Semester 2, 2021 [Normal day] - Camperdown/Darlington, Sydney

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.

Unit details and rules

Unit code QBUS6860
Academic unit Business Analytics
Credit points 6
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

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: LO1 LO6 LO5 LO4 LO3 LO2
Assignment Individual Assignment
Individual assignment (provided at the start of the start of the semester)
60% Week 13 5000
Outcomes assessed: LO1

Assessment summary

  • 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.

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:

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

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.

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 to Visual Data Analytics Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 02 Data Types and Visualisation Types Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 03 Exploratory Data Analysis using Visualisation Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 04 Avoiding Biases and Dark Patterns Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 05 Storytelling with Data Visualisation Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 06 Interactive Visualisation and Human Data Interaction Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 07 Visualisations for Decision-making Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 09 Visualising Networks Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 10 Visualising Processes Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 11 Visualising Time-Series Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 12 Visualising with Machine Learning Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1
Week 13 Future of Data Visualisation Lecture (2 hr) LO1
Tutorial activity Tutorial (1 hr) LO1

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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

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

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.