Course Title: Data Visualisation

Part A: Course Overview

Course Title: Data Visualisation

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2237

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2016

MATH2237

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021

Course Coordinator: Dr James Baglin

Course Coordinator Phone: +61 3 9925 6118

Course Coordinator Email: james.baglin@rmit.edu.au

Course Coordinator Location: Building 15, Level 3, Room 13, City campus

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH2200 Introduction to Probability and Statistics, 

MATH2201 Basic Statistical Methodologies and MATH2382 Data Pre-processing are assumed knowledge. 

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. Formative module quizzes will be used to self-monitor and 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.

Communication

  • 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 successful 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 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 will be provided in a blended approach of both face-to-face and online activities. 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.  

Authentic and industry-relevant learning is critical to this course and you will be encouraged to critically compare and contrast what is happening in your context and in industry, and to use your insights.   

Social learning is another important component and you are expected to participate in class and group activities, share drafts of work and resources and give and receive peer feedback. You will be expected to work efficiently and effectively with others to achieve outcomes greater than those that you might have achieved alone.   

Above all, the learning activities are designed to maximize the likelihood that you will not only understand the course learning resources but also apply that learning to improving your own practice, for example by producing real-world artefacts and engaging in scenarios and case studies.


Overview of Learning Resources

All course content, notes and learning materials will be available through Canvas. . 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.  

An online communication tool will be used to create a learning community outside of class. This tool will allow teaching staff and students to communicate asynchronously for Q&A, critical discussion, peer-to-peer support, resource sharing and feedback.

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


Overview of Assessment

Note: This course has no hurdle requirements. 

Assessment Tasks

Early Assessment Task:  Assignment 1

Data Visualisation Storytelling   
Weighting 30%  
This assessment task supports CLOs 1 & 2  

Assessment Task 2:  Assignment 2 

Data Visualisation Critique   
Weighting 30%  
This assessment task supports CLOs 1, 2, 3 & 4.  


Assessment Task 3: 
Assignment 3 

Storytelling with Open Data  
Weighting 40%  
This assessment task supports CLOs 1, 2, 3, 4, 5, 6 

  

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