DATA424-26S2 (C) Semester Two 2026

Information Is Beautiful

15 points

Details:
Start Date: Monday, 13 July 2026
End Date: Sunday, 8 November 2026
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 26 July 2026
  • Without academic penalty (including no fee refund): Sunday, 27 September 2026

Description

This course will introduce students to the truthful art of visualizing data. The students will use an iterative design process to create visualizations that are truthful, functional, beautiful, insightful and enlightening. The lectures will consist of presentations, critiques, in-class exercises and discussions. This course will enable students to select appropriate visualization methods for their data and solve practical data science communication problems. They will consider the context and the indented reader to focus the story their data will tell. The students will learn to use the Tableau software, which will be made available for their own computers within the framework of this course. The course will provide a supportive environment in which students can experiment with the aesthetics of data visualization. Students will need to be familiar with basic data manipulation principles and the process of data gathering and cleaning.

The course structure will first introduce the students to some fundamental principles and problems in data visualization that can be directly applied to common data science problems. The students will be encouraged to use the Tableau software for their visualizations.

·      Introduction to Information Visualization
·      Introduction to Tableau
·      Introduction to Typography
·      Tables
·      Simple Charts
·      Visualization Distributions
·      Revealing Change
·      Seeing Relationships
·      Mapping Data
·      Uncertainty and Significance
·      Visual Perception
·      Infographics

In addition to the assignments, the students will be asked to work on exercises that will not be graded. Each exercise will need to be completed by the next lecture session and will need to be submitted through Learn.

Learning Outcomes

At the end of this course, students will be able to:
• Demonstrate ability to use data visualization software
• Demonstrate knowledge of the range of data visualisation methods
• Show competency applying this knowledge to select the appropriate visualization method depending on the selected data and the target audience
• Master the construction of a narrative through data visualization
• Show competency in criticizing data visualizations

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

Critically competent in a core academic discipline of their award

Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

Employable, innovative and enterprising

Students will develop key skills and attributes sought by employers that can be used in a range of applications.

Engaged with the community

Students will have observed and understood a culture within a community by reflecting on their own performance and experiences within that community.

Globally aware

Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.

Prerequisites

Subject to approval of the Head of Department of Mathematics and Statistics.

Timetable 2026

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 10:00 - 12:00 Jack Erskine 101
13 Jul - 23 Aug
7 Sep - 18 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Friday 11:00 - 12:00 Jack Erskine 010 Computer Lab (17/7-21/8, 11/9-16/10)
Jack Erskine 001 Computer Lab (17/7-21/8, 11/9-16/10)
13 Jul - 23 Aug
7 Sep - 18 Oct

Course Coordinator

Christoph Bartneck

Dr. Christoph Bartneck is a professor at the University of Canterbury. He has a background in Industrial Design and Human-Computer Interaction, and his projects and studies have been published in leading journals, newspapers, and conferences. His interests lie in the fields of Human-Computer Interaction, Science and Technology Studies, and Visual Design. More specifically, he focuses on the effect of anthropomorphism on human-robot interaction. As a secondary research interest he works on bibliometric analyses, agent based social simulations, and the critical review on scientific processes and policies. In the field of Design Christoph investigates the history of product design, tessellations and photography.

Assessment

Assessment Due Date Percentage  Description
Attendance 10% Attendance will be taken at every lecture
Exercises 10% The exercises are worth a total of 10% of the final grade. Work on these exercises will start in the first week.
Assignment 10 Aug 2026 20% Fun with flags
Assignment 17 Aug 2026 20% The chase
Assignment 14 Sep 2026 20% Road crashes across time
Assignment 12 Oct 2026 20% Injuries road crashes


This course consists of lectures and small hand-on assignments. You will use the Tableau software for most assignments. The data sets necessary for the assignments will be provided.

Exercises
The exercises are worth a total of 10% of the final grade. They are being graded as Pass/Fail.

1 The solar system Pass/Fail 22/07/2026 09:00h
2 Heat map table        Pass/Fail 03/08/2026 09:00h
3 Nye vs Ham        Pass/Fail 07/09/2026 09:00h
4 Social relationships Pass/Fail 21/09/2026 09:00h
5 Rectangles                Pass/Fail 28/09/2026 09:00h
6 Nutritious facts        Pass/Fail 05/10/2026 09:00h

Additional Course Outline Information

Academic integrity

Please ensure that all submissions comply with the University’s Academic Integrity Policy. All work may be reviewed using Turnitin, and any form of plagiarism will be treated as academic misconduct.

Students are expected to complete all work independently and to appropriately acknowledge any sources used. To support transparency and learning, you are required to keep draft versions of your work throughout the assignment. It is important that you can document your design and development process. For example, you should save iterative versions of your work as you progress through the assignment. Do not delete intermediate drafts, as they may be required to evidence your process if needed.

Grade moderation

All assignments and exercises will be graded using these criteria:
• Truthful: is it based on thorough and honest research
• Functional: is it an accurate depiction and can the audience draw meaningful conclusions
• Beautiful: is it aesthetically pleasing
• Insightful: does the visualisation reveal evidence that would be difficult to see otherwise
• Enlightening: does it change the mind of the audience

Late submission of work

All submissions for assignments will be done through Learn.

Late submissions to the assignments are handled through the special considerations process. Late submissions to the exercises will be managed by the course coordinator following the process and policies similar to those for special considerations.

For the first two weeks, students who experience exceptional circumstances may contact the Course Coordinator to discuss possible accommodations. Examples of exceptional circumstances include a delayed arrival in New Zealand or other unforeseen events that prevent timely participation. Supporting evidence, such as a flight itinerary or travel documentation, will be required. Requests for consideration should be submitted as soon as possible and no later than five working days after the exercise due date. For medical circumstances, the same requirements as for special considerations, including deadlines and supporting evidence, apply and will be handled by the course coordinator.

Participation in the lectures

To help create an engaging and focused learning environment, a no-screens policy will generally apply during lectures. This allows everyone to concentrate fully on the discussions, activities, and interactions that take place in class, while reducing distractions from emails, messages, and other online content. Research has shown that minimising digital distractions can improve attention, participation, and learning outcomes.

There will be occasions when students are asked to use their laptops to complete specific in-class activities, and clear instructions will be provided when this is required. During laboratory sessions, students will use the provided computers or their own devices to work on assignments, exercises, and other tutorials. Students with special needs will be allowed to use all required devices to support their learning process.

Attendance as a whole is worth 10% of your grade. The following points will be given for your attendance at a lecture:

• Present: 2
• Excused: 2
• Late: 1
• Absent: 0

To ensure fairness and consistency for all students, an excused status is available in cases where exceptional circumstances have prevented attendance. The criteria are the same as those used for approved late submissions (see below).

Students experiencing exceptional circumstances are encouraged to contact the Course Coordinator and submit a request for consideration. Appropriate supporting evidence will be required, consistent with the documentation requirements for Special Consideration applications. Requests should be submitted as soon as possible, either at the time of the lecture or no later than five working days after the lecture date.

We understand that students may face unexpected challenges during the semester. Each request will be considered on its individual merits. However, joining the course late does not automatically qualify as an exceptional circumstance. Students will need to provide a valid reason and supporting evidence explaining why they were unable to attend.

Generative AI

In some of the assignments, you are permitted to use generative artificial intelligence (AI) solely for the purpose of generating decorative images. No other use of generative AI is permitted. To assist with maintaining academic integrity, you must appropriately acknowledge any use of generative AI in your work. Please include a statement of acknowledgement/AI declaration with your work, clearly indicating which AI tools were used and how they contributed to your assessment. The more AI is used in your submissions, the less the submission can be considered your contribution, which does have a negative impact on your grade.

Indicative Fees

Domestic fee $1,247.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

All DATA424 Occurrences

  • DATA424-26S2 (C) Semester Two 2026