DATA424-22S2 (C) Semester Two 2022

Information Is Beautiful

15 points

Start Date: Monday, 18 July 2022
End Date: Sunday, 13 November 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 31 July 2022
  • Without academic penalty (including no fee refund): Sunday, 2 October 2022


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

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.

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.


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

Timetable 2022

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 11:00 - 13:00 E12
18 Jul - 28 Aug
12 Sep - 23 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Thursday 09:00 - 10:00 Jack Erskine 248 Computer Lab
18 Jul - 28 Aug
12 Sep - 23 Oct

Course Coordinator / Lecturer

Christoph Bartneck

Dr. Christoph Bartneck is an associate 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.


This course consists of lectures and small hand-on assignments. You are encouraged to use the Tableau software for most assignments. The data sets necessary for the assignments will be provided. Four out of 10 assignments will be graded. Assignments will be graded on:

• 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

Attendance and participation in exercises posted on Learn contribute 20% to the final grade. The students will be given four assignments that each are count for 20% of the final grade.

Additional Course Outline Information


By default there will be a no-screens policy during the lectures. This means that you are not allowed to open your laptop or use your mobile device. For specific tasks the students will be asked to use their laptop computer to complete in lectures tasks. During the lab hours the students are encouraged to bring their devices to work on their assignments.

Late submission of work

All submissions for assignments will be done trough Learn and must be completed before the deadlines setup in Learn.

Indicative Fees

Domestic fee $1,051.00

* 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

  • DATA424-22S2 (C) Semester Two 2022