Course Title: Data Visualisation

Part A: Course Overview

Course Title: Data Visualisation

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2016


City Campus


171H School of Science


Sem 1 2018,
Sem 1 2019

Course Coordinator: Dr James Baglin

Course Coordinator Phone: +61 3 9925 6118

Course Coordinator Email:

Course Coordinator Location: 08.09.69

Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH2200 Introduction to Probability and Statistics

MATH2201 Basic Statistical Methodologies

MATH2202 Data Preparation for Analytics

Familiarity with R will be highly beneficial

Course Description

Learn how to create compelling data visualisations that tell the story behind the data. The course will begin with a focus on designing visualisations appropriate to the information in the data and the audience to whom you are communicating the results. This course will introduce the use of leading open source software packages to enable clear and insightful visualisations of complex data. You will develop and apply your data visualisation knowledge to complex, big, real-world data. You will also explore cutting-edge, cloud-based applications to bring your visualisations to life.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.)

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Science (Mathematics) and BH119 Bachelor of Analytics (Honours):

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.


On completion of this course you should be able to:


  1. Design visualisations that represent the relationships contained in complex data sets and adapt them to highlight the ideas you want to communicate
  2. Support your visualisations with written and verbal explanations on their interpretation. 
  3. Use leading open source software packages to create and publish visualisations that enable clear interpretations of big, complex and real world data
  4. Use cloud-based applications to bring your data visualisations to life by adding interactivity.
  5. Identify the statistical analysis needed to validate the trends present in data visualisations.

Overview of Learning Activities

Course learning activities take place both online and face-to-face. You are required to have regular and reliable access to a high-speed internet connection. Online course notes and materials replace traditional lectures and labs. Face-to-face class time is used for hands-on demonstrations, discussions, and working collaboratively with other students on exercises and problems.

Students are highly recommended to bring along a portable computing device to class, preferably a laptop, with WiFi access to the RMIT University network. Those bringing tablets are encouraged to bring a portable keyboard and mouse. Students without portable computing devices may be able to use room computers, if available, or share with other students. All course materials and learning activities will be available online. Students unable to bring a portable computing device to class will be able to work through material in their own time.

Students will develop their visualisation skills through pratice exercises and assignments that require the development and application of their knowledge to practical and real-world data visualization tasks.

Students will stay in communication and actively participate in course discussions outside of class time through an online communication tools.


Total study hours

During the 12 week teaching period, this course will be comprised of two hours of face-to-face class time and one hour of optional supervised self-study (24 hours). Students will also be expected to dedicate at least 60 hours to self-directed learning, including practice exercises, assignments and revision, spread across the entire 16 weeks of the semester (12 study weeks, 1 SWOT vac week and 3 exam weeks).

Overview of Learning Resources

All course content, notes and learning materials will be available through Canvas. The course will feature an online textbook. Course assessment and major announcements will take place through the Canvas site on MyRMIT Studies. The course Canvas site will also house all the important links to the course resources.

There are no prescribed textbooks for this course. An online textbook will contain the necessary course notes. The online course textbook will contain a list of recommended data visualisation references.

This course will focus on the use of leading open source software packages used for data visualisation. Instructions for obtaining access to this software will be detailed on the course website.

Overview of Assessment

☒This course has no hurdle requirements.

Assessment Tasks:


Early Assessment Task:  Assignment 1

An introductory data visualisation task and summary due in the first quarter of the semester.

Weighting 15%

This assessment task supports CLOs 1, 2 & 3


Assessment Task 2:  Assignments 2 & 3  

Additional data visualisation assignments. 

Weighting 35%

This assessment task supports CLOs 1, 2, 3, 4 and 5


Assessment Task 3: End of Semester Exam

A final examination during exam period Weighting 50% 

This assessment supports CLOs 1, 2,3, 4 and 5.


All course marks and feedback will be provided online through the course learning management system (Canvas).