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)

MATH2270

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2016

MATH2270

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 2 2017,
Sem 1 2018,
Sem 2 2018,
Sem 1 2019,
Sem 2 2019

Course Coordinator: Dr James Baglin

Course Coordinator Phone: +61 3 9925 6118

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

Course Coordinator Location: 008.09.069


Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1324 Introduction to Statistics and MATH2349 Data Preprocessing


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 exam will test your knowledge gained throughout the course.


Objectives/Learning Outcomes/Capability Development

 This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

 

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 completion of this course you should be able to:

  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

This course runs online with regular face-to-face support available. You are required to have regular and reliable access to a high-speed internet connection. Weekly online readings, short videos, exercises and online discussion replace traditional lectures and labs. Optional face-to-face drop-in classes are available for students who need extra support or interaction with teaching staff and peers. Practice exercises, online discussion, major assignments, and revision quizzes will support student learning and provide feedback. You will also complete an end of semester exam to test your knowledge gained throughout the course. 

Total Study Hours: 

Total study hours will vary depending on the students’ prior knowledge and experience. At the minimum, you should expect to dedicate 4 hours to weekly tasks for each of the 12 teaching weeks (48 hours), 2 hours per week for assignments (24 hours) and 6 hours per week during the four-week revision and exam period (24 hours).


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.

 

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


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 in the course textbook.


http://rmit.libguides.com/mathstats

 


Overview of Assessment

This course has no hurdle requirements.


Assessment Tasks:


Early Assessment Task: Assignment 1

Data Visualisation Storytelling Vodcast
Weighting 10%
This assessment task supports CLOs 1 & 2

 

Assessment Task 2: Assignment 2

Deconstruct, Reconstruct Web Report
Weighting 20%
This assessment task supports CLOs 3, 4 & 5 in addition to previous CLOs.

 

Assessment Task 3: Assignment 3

Storytelling with Open Data: Your Turn
Weighting 30%
This assessment task supports CLOs 6 & 7 in addition to previous CLOs.

 

Assessment Task 4: End of Semester Exam

A final examination during exam period.
Weighting 40%
This assessment supports all CLOs.

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