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,
Sem 1 2020

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 design intuitive, accessible and compelling data visualisations that communicate the story behind the data and address practical, real-world problems. During this course you will discover how to integrate interdisciplinary knowledge from the major fields that inform and shape data visualisation practice, including information visualisation, visual perception and psychology, statistics, computer science and art. By working through your own visualisations and studying the work of others, you will develop a design-driven approach to data visualisation. Specifically, you will learn to clearly articulate a targeted audience and design goal, source and prepare appropriate data, choose an appropriate design and bring your design to life using open source, web-based, interactive data visualisation technology. You will learn to identify and apply practical, evidence-based strategies that enhance effective data visualisation through a synthesis of the research literature and expert experience. Throughout the course, you will also learn how to critique data visualisations, navigate professional and ethical issues, apply methods of storytelling with data, and build apps, including dashboards. Major course assignments will require you to apply your learning to solve authentic problems and a final test will assess your knowledge gained throughout the course.

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 will achieve the following Course Learning Outcomes (CLOs):

  1. identify your target audience and determine a data visualisation design goal;
  2. use data visualisation and storytelling techniques, including verbal, written and interactive features to help engage your audience and leave a lasting impression;
  3. reflect on the major professional, ethical and integrity-based issues that arise during the practice of data visualisation;
  4. apply and integrate expert-informed best practice and research knowledge to enhance the effectiveness of your data visualisations and critique the work of others;
  5. conceptualise multiple data visualisation designs and determine the most appropriate strategy to achieve your goal taking into account your audience and the nature of the data;
  6. source, review and prepare data required for your data visualisation;
  7. and program leading, web-based, open-source, interactive data visualisation technology to build, deploy and disseminate your data visualisations, including applications and dashboards.

Overview of Learning Activities


Course learning activities take place both face-to-face and online. 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. All course materials and learning activities will be available online.

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

Students will stay in communication and actively participate in course discussions outside of class time through 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 (36 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 is structured around a free online textbook authored by the course coordinator. 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.

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.

The RMIT Library offers additional learning resources. Details are available on the Mathematics and Statistics Library Subject Guide.

Overview of Assessment

This course has no hurdle requirements.

Assessment Tasks:


Early Assessment Task:  Assignment 1

Data Visualisation Storytelling Vodcast

Weighting 20%

This assessment task supports CLOs 1 & 2


Assessment Task 2:  Assignments 2

Data Visualisation Critique Web Report 

Weighting 20%

This assessment task supports CLOs 1-7.


Assessment Task 3:  Assignments 3

Storytelling with Open Data

Weighting 30%

This assessment task supports CLOs 1-7.


Assessment Task 4: End of Semester Exam

A final examination during exam period

Weighting 30% 

This assessment supports all CLOs.


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