Course Title: Data Visualisation and Communication

Part A: Course Overview

Course Title: Data Visualisation and Communication

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,
Sem 1 2020,
Sem 2 2020

Flexible Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2404

RMIT Online

Postgraduate

171H School of Science

Internet

JanJun2020 (KP2)

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 Applied Analytics and MATH2349 Data Wrangling are assumed knowledge but are not enforced pre-requisites.


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.


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.


Course Learning Outcomes

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 is offered in two different delivery modes: On Campus and Online. While the Learning Outcomes remain the same, the learning activities and the assessment tasks vary with location or mode of delivery.

On Campus

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.

Online        

This course uses highly structured learning activities to guide your learning process and prepare you for your assessments. The activities are a combination of individual, peer-supported and facilitator-guided activities, and where possible project-led, with opportunities for feedback throughout. 

 

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.

 

Total Study Hours: 

On campus

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).

Online

During the 6-week teaching period, students are expected to commit 15-20 hours of study each week. This will be a combination of guided and self-directed study.


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.

On Campus

A free online textbook authored by the course coordinator will contain the course notes. Additional required readings will be detailed and accessible through Canvas. 

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.

Online

Each learning activity contains the core resources, such as videos, podcasts, readings, templates, articles, industry tools and/or communities that you need to complete that activity, or links to those resources. 

 

Additional learning resources designed into the course, will be clearly marked as supplemental. If your course teaching team finds additional resources during course delivery which they think can support or be of interest to the class cohort, these will be made available as required during the teaching period. 

 

In your class environment, besides your learning activities you will also find 

  • All assessment briefs 
  • A course information page with a study schedule
  • Various communication tools to facilitate collaboration with your peers and facilitators, and to share information 

 

Learning Resources are also available online through RMIT Library databases and other facilities. If you require assistance with the RMIT library facilities contact the Business Liaison Librarian for your school. Contact details for Business Liaison Librarians are located on the RMIT Library website. 


Overview of Assessment

Overview of Assessment

This course has no hurdle requirements.

On Campus

Assessment Tasks:

Early Assessment Task: Assignment 1

Data Visualisation Storytelling Vodcast
Weighting 20%
This assessment task supports CLOs 1 & 2

 

Assessment Task 2: Assignment 2

Data Visualisation Critique Web Report
Weighting 30%
This assessment task supports CLOs 3, 4 & 5 in addition to previous CLOs.

 

Assessment Task 3: Assignment 3

Storytelling with Open Data
Weighting 40%
This assessment task supports CLOs 6 & 7 in addition to previous CLOs.

 

Assessment Task 4: Module Quizzes

Complete an online quiz for each course module.
Weighting 10%
This assessment supports all CLOs.

 

Online

Early Assessment Task: Assignment 1

Data Visualisation Vodcast
Weighting 30%
This assessment task supports CLOs 1, 2, 3, 4

 

Assessment Task 2: Assignment 2

Storytelling with Open Data
Weighting 50%
This assessment task supports CLOs 5, 6 & 7 in addition to previous CLOs.

 

Assessment Task 3: Module Quizzes

Complete an online quiz for each of the six course modules. 
Weighting 20%
This assessment supports all CLOs.

 

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