Outline

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.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	QBUS5001 or QBUS5002
Corequisites ?	None
Prohibitions ?	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	Junbin Gao, junbin.gao@sydney.edu.au

Type	Description	Weight	Due	Length
Small continuous assessment	Weekly Participation Assignments Short continuous assessment	10%	Multiple weeks	Varied
Outcomes assessed:
Assignment	Individual Assignment Preliminary Visualisation Tasks	45%	Week 08 Due date: 23 Sep 2022 at 16:00 Closing date: 30 Sep 2022	Varied
Outcomes assessed:
Assignment	Group Project Group Project to complete assigned project tasks.	45%	Week 13 Due date: 01 Nov 2022 at 16:00 Closing date: 08 Nov 2022	Varied
Outcomes assessed:
= group assignment ?

Assessment summary

Short Quizzes: There are 2 short quizzes test online worth 5% marks each.
One Individual Written Assignment: Individual Assignment, worth 45% marks. IMPORTANT: You must use Python for all visualisations you will produce in your individual assignment.
One Group Project Assignment: Written Report and Coding. Worth 45% Marks. IMPORTANT: You must use Python for all visualisations you will produce in the group project.

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 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:

Follow the relevant university and school policies.

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	Introduction to Visual Data Analytics	Lecture (2 hr)
Week 01	Tutorial activity	Tutorial (1 hr)
Week 02	Data Types and Visualisation Types (I)	Lecture (2 hr)
Week 02	Tutorial activity	Tutorial (1 hr)
Week 03	Data Types and Visualisation Types (II)	Lecture (2 hr)
Week 03	Tutorial activity	Tutorial (1 hr)
Week 04	Data Management	Lecture (2 hr)
Week 04	Tutorial activity	Tutorial (1 hr)
Week 05	Exploratory Data Analysis using Visualisation (EDA I)	Lecture (2 hr)
Week 05	Tutorial activity	Tutorial (1 hr)
Week 06	Transformation, Avoiding Biases and Dark Patterns (EDA II)	Lecture (2 hr)
Week 06	Tutorial activity	Tutorial (1 hr)
Week 07	In-semester exam period, Q&A for Assignments	Lecture (2 hr)
Week 08	Storytelling with Data Visualisation	Lecture (2 hr)
Week 08	Tutorial activity	Tutorial (1 hr)
Week 09	Interactive Visualisation & Human Data Interaction	Lecture (2 hr)
Week 09	Tutorial activity	Tutorial (1 hr)
Week 10	Visualising Time-Series and Processes	Lecture (2 hr)
Week 10	Tutorial activity	Tutorial (1 hr)
Week 11	Visualising Networks	Lecture (2 hr)
Week 11	Tutorial activity	Tutorial (1 hr)
Week 12	Data complexities and robust analysis	Lecture (2 hr)
Week 12	Tutorial activity	Tutorial (1 hr)
Week 13	Decision-making and Machine Learning with 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, 2022 this unit will continue using Python as the main programming platform. It is your responsibility to make sure that you have all required 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

Responding to student feedback

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

This unit will continue to use Python as the main platform in 2022S2 since 2022S1 and introduce a group project as the assessment component.

Additional information

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

QBUS6860: Visual Data Analytics

Semester 2, 2022 [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