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Unit outline_

QBUS5010: Intro to Dashboarding and Data Visualisation

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

Dashboards are digestible and real-time visualisation systems, which can produce valuable insights about business processes. These dashboards may even form a core component of future business processes and decision making. Creating such visualisations is a challenging task requiring the creator to have wide ranging knowledge and skills in such fields as user experience design, statistics, visualisation, databases, web technologies and programming. This unit aims to equip students with a working knowledge of dashboard systems. Students learn how dashboards systems work, how they are built and to analyse dashboard designs using principles of visual analytics.

Unit details and rules

Academic unit Business Analytics
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

The unit does not assume any prior knowledge of visual analytics

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Stephen Tierney, stephen.tierney@sydney.edu.au
Type Description Weight Due Length
Tutorial quiz Individual assignment
n/a
30% Multiple weeks fortnightly
Outcomes assessed: LO1 LO4 LO3 LO2
Assignment Individual report
n/a
40% Week 07
Due date: 18 Sep 2022 at 19:00
n/a
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment group assignment Group project
n/a
30% Week 13 n/a
Outcomes assessed: LO1 LO2 LO3 LO4
group assignment = group assignment ?

Assessment summary

Individual assignment: Students will complete fortnightly programming exercises.

Individual report: Students will practice their design thinking, user experience and visualisation skills by creating a design brief for a dashboard.

Group project: In small groups students will integrate their design, user experience and programming skills to build a working dashboard.

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.

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 Python Introduction 1 Lecture (2 hr) LO4
Week 01 Visualisation Context. Python Introduction 2. Lecture (2 hr) LO1 LO2 LO3 LO4
Python Introduction 1 Tutorial (1 hr) LO4
Week 02 Data - Structure, Organisation and Access. Data Handling with Python 1. Lecture (2 hr) LO1 LO2 LO3 LO4
Python Introduction 2 Tutorial (1 hr) LO4
Week 03 Visualisation Types. Plotting with Python 1. Lecture (2 hr) LO1 LO2 LO3 LO4
Data Handling with Python 1 Tutorial (1 hr) LO4
Week 04 Capturing Attention. Data Handling with Python 2. Lecture (2 hr) LO1 LO2 LO3 LO4
Plotting with Python 1 Tutorial (1 hr) LO1 LO4
Week 05 User Experience. Plotting with Python 2. Lecture (2 hr) LO1 LO2 LO3 LO4
Data Handling with Python 2 Tutorial (1 hr) LO4
Week 06 Web and Dashboard Technology 1 Lecture (2 hr) LO1 LO2 LO3 LO4
Plotting with Python 2 Tutorial (1 hr) LO1 LO4
Week 07 Web and Dashboard Technology 2 Lecture (2 hr) LO1 LO2 LO3 LO4
Dashboarding with Python 1 Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 08 Web and Dashboard Technology 3 Lecture (2 hr) LO1 LO2 LO3 LO4
Dashboarding with Python 2 Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 09 Project Discussions Workshop (2 hr) LO1 LO2 LO3 LO4
Dashboarding with Python 3 Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 10 Advanced Topic 1 Lecture (2 hr) LO1 LO2 LO3 LO4
Dashboarding with Python 4 Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 11 Advanced Topic 2 Lecture (2 hr) LO1 LO2 LO3 LO4
Advanced Topic 1 Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 12 Project Discussions Workshop (2 hr) LO1 LO2 LO3 LO4
Advanced Topic 2 Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 13 Project Presentations Workshop (2 hr) LO1 LO2 LO3 LO4

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.

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. Students will be able to distinguish between visualisations and dashboards as well as utilise dashboards by converting visualisations to dashboards and replicating existing dashboards
  • LO2. Students will be able to differentiate between different principles of dashboard building as well as to apply these principles to build dashboards themselves
  • LO3. Students will be able to critically analyse dashboard designs using principles of visual analytics and make well-argued recommendations for improvement
  • LO4. Students will be able to design dashboards using a range of visualisation types, statistical, and programming techniques

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.

This is the first offering.

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.