CCQ104 Visualising Data
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | CCQ104 |
|---|---|
| Equivalent(s): | CCN104 |
| Credit points: | 6 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| Domestic tuition unit fee | $1,488 |
| International unit fee | $2,022 |
Unit Outline: Session-2B 2024, QUT Online, Online
| Unit code: | CCQ104 |
|---|---|
| Credit points: | 6 |
| Equivalent: | CCN104 |
Overview
This foundational unit provides you with some fundamental knowledge and skills in data visualisation, such as design, techniques and methods for the visual communication of complex datasets. To produce the simplest bar graph and scatter plot to highly complex network diagrams requires an understanding of the principles of visual communication and data analytics. Across the communication and media industries there is a growing demand for communication specialists to be able to effectively communicate complex datasets to non-specialist audiences. Knowing how to make sense of data for diverse audiences, through appropriate visual representation, is a key skill for contemporary communication professionals.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain the principles of visual information design and how they can be used to provide insights into datasets.
- Create interactive visualisations of data using industry standard applications.
- Analyse and critique visual information designs.
Content
This unit will explore topics such as:
- the theory and principles of visual communication and information design
- methods and techniques for working with data
- common types of visualisation from simple histograms, scatter plots and treemaps to complex networks and sanky diagrams
- industry-standard software applications used to create interactive data visualisations.
Learning Approaches
This unit will be delivered fully online and may include intensive delivery. It will be facilitated by a unit coordinator using a flipped learning approach. This approach requires you to complete online pre-workshop activities independently, prior to attending the online workshop. Indicative learning experiences in this unit may include:
- participating in online discussions
- viewing mini lecture videos
- reading book chapters and scholarly and industry articles
- listening to podcasts
- practising and refining your data visualisation skills
- analysing real world data visualisations
- using industry-standard software applications for data visualisation
- critiquing visualisations
- giving and receiving peer feedback
- reflecting on learning.
Feedback opportunities from members of the teaching team and your peers will be integrated into the unit as outlined in the Feedback to students section of this unit outline.
Feedback on Learning and Assessment
You will receive feedback on your learning in a variety of ways, including:
- Informal formative feedback will be provided via quizzes, self-assessment tools, peer feedback and through individual or whole of class feedback, the debriefing of learning activities or via comments in online communities.
- Formal written or recorded feedback will be received on both formative and summative assessment tasks, in addition to the grade on the Criterion Reference Assessment sheet.
Feedback on your formative assessment task will be received prior to the submission of your summative assessment task.
Assessment
Overview
You will be required to complete two assessment items: a critique of an existing program of visual information designs and maintain a visual information design graphic workbook. The assessment in the unit has been designed to provide you with the opportunity to demonstrate your ability to analyse and critique visual information design in the context of datasets and to produce visual information designs that provide insight into and effectively communicate complex datasets.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Graphic Workbook
You will produce a number of visualisations using a provided dataset. You will then analyse the efficacy of different visualisation methods in respect to the way visual principles influence communication.
This is an assignment for the purposes of an extension.
Assessment: Graphic Workbook
You will produce a series of visual information designs that provide an insight into a complex data set for a number of different clients. You will present your work as a portfolio of interactive visualisations using industry standard software. This will be accompanied by a written document which outlines the rationale for selection of methods of visualisation and how these have allowed you to gain insights into the data set.
This is an assignment for the purposes of an extension.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
- Cite|Write
- Unit site
Recommended texts includes:
- Lima, M. (2011). Visual complexity?: mapping patterns of information (1st ed.). New York: Princeton Architectural Press.
- McCandless, D. (2009). Information is beautiful. London: Collins.
- Mollerup, Per. (2015) Data Design: Visualising Quantitied, Locations, Connections. New York: Bloomsbury.
- Tufte, E. (1990). Envisioning information (2nd printing with revisions.). Cheshire, Conn.: Graphics Press.
Risk Assessment Statement
You are advised to back-up your digital files on a regular basis to ensure work is not lost if there is a hardware failure. Information about the free file storage provided by QUT for students is available on the Storing your files page.
There are no out of the ordinary risks associated with this unit.
Unit Outline: Session-4B 2024, QUT Online, Online
| Unit code: | CCQ104 |
|---|---|
| Credit points: | 6 |
| Equivalent: | CCN104 |
Overview
This foundational unit provides you with some fundamental knowledge and skills in data visualisation, such as design, techniques and methods for the visual communication of complex datasets. To produce the simplest bar graph and scatter plot to highly complex network diagrams requires an understanding of the principles of visual communication and data analytics. Across the communication and media industries there is a growing demand for communication specialists to be able to effectively communicate complex datasets to non-specialist audiences. Knowing how to make sense of data for diverse audiences, through appropriate visual representation, is a key skill for contemporary communication professionals.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain the principles of visual information design and how they can be used to provide insights into datasets.
- Create interactive visualisations of data using industry standard applications.
- Analyse and critique visual information designs.
Content
This unit will explore topics such as:
- the theory and principles of visual communication and information design
- methods and techniques for working with data
- common types of visualisation from simple histograms, scatter plots and treemaps to complex networks and sanky diagrams
- industry-standard software applications used to create interactive data visualisations.
Learning Approaches
This unit will be delivered fully online and may include intensive delivery. It will be facilitated by a unit coordinator using a flipped learning approach. This approach requires you to complete online pre-workshop activities independently, prior to attending the online workshop. Indicative learning experiences in this unit may include:
- participating in online discussions
- viewing mini lecture videos
- reading book chapters and scholarly and industry articles
- listening to podcasts
- practising and refining your data visualisation skills
- analysing real world data visualisations
- using industry-standard software applications for data visualisation
- critiquing visualisations
- giving and receiving peer feedback
- reflecting on learning.
Feedback opportunities from members of the teaching team and your peers will be integrated into the unit as outlined in the Feedback to students section of this unit outline.
Feedback on Learning and Assessment
You will receive feedback on your learning in a variety of ways, including:
- Informal formative feedback will be provided via quizzes, self-assessment tools, peer feedback and through individual or whole of class feedback, the debriefing of learning activities or via comments in online communities.
- Formal written or recorded feedback will be received on both formative and summative assessment tasks, in addition to the grade on the Criterion Reference Assessment sheet.
Feedback on your formative assessment task will be received prior to the submission of your summative assessment task.
Assessment
Overview
You will be required to complete two assessment items: a critique of an existing program of visual information designs and maintain a visual information design graphic workbook. The assessment in the unit has been designed to provide you with the opportunity to demonstrate your ability to analyse and critique visual information design in the context of datasets and to produce visual information designs that provide insight into and effectively communicate complex datasets.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Graphic Workbook
You will produce a number of visualisations using a provided dataset. You will then analyse the efficacy of different visualisation methods in respect to the way visual principles influence communication.
This is an assignment for the purposes of an extension.
Assessment: Graphic Workbook
You will produce a series of visual information designs that provide an insight into a complex data set for a number of different clients. You will present your work as a portfolio of interactive visualisations using industry standard software. This will be accompanied by a written document which outlines the rationale for selection of methods of visualisation and how these have allowed you to gain insights into the data set.
This is an assignment for the purposes of an extension.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
- Cite|Write
- Unit site
Recommended texts includes:
- Lima, M. (2011). Visual complexity?: mapping patterns of information (1st ed.). New York: Princeton Architectural Press.
- McCandless, D. (2009). Information is beautiful. London: Collins.
- Mollerup, Per. (2015) Data Design: Visualising Quantitied, Locations, Connections. New York: Bloomsbury.
- Tufte, E. (1990). Envisioning information (2nd printing with revisions.). Cheshire, Conn.: Graphics Press.
Risk Assessment Statement
You are advised to back-up your digital files on a regular basis to ensure work is not lost if there is a hardware failure. Information about the free file storage provided by QUT for students is available on the Storing your files page.
There are no out of the ordinary risks associated with this unit.