Part A: Course Overview

Course Title: Data Visualisation with R

Credit Points: 12.00

Important Information:

 

 


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,
Sem 1 2022,
Sem 1 2023,
Sem 2 2023,
Sem 1 2024,
Sem 2 2024,
Sem 1 2025

MATH2459

RMIT University Vietnam

Undergraduate

171H School of Science

Face-to-Face

 Viet1 2025

MATH2460

RMIT Vietnam Hanoi Campus

Undergraduate

171H School of Science

Face-to-Face

 Viet1 2025

Course Coordinator: Dr. James Baglin

Course Coordinator Phone: +61 3 9925 6118

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

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Recommended Prior Study 

For BP350 Bachelor of Science and BP083P23 Bachelor of Science (Mathematics), You should have satisfactorily completed or received credit for the following course/s before you commence this course: 

For BP340 Bachelor of Data Science, you should have satisfactorily completed following course/s before you commence this course:

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer. 

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning. 

Please follow the link for further information on how to apply for credit for prior study or experience. 

Recommended Concurrent Study 

It is recommended to undertake the following course at the same time as this course as it contains areas of knowledge and skills which are implemented together in practice. 

Alternatively, if you have the equivalent skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.  

Please contact your course coordinator for further details. 

Assumed Knowledge 

You are assumed to have knowledge of fundamental statistical concepts when participating in this course. 


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.

If you are enrolled in this course as a component of your Bachelor Honours Program, your overall mark will contribute to the calculation of the Weighted Average Mark (WAM).  

See the WAM information web page for more information. 


Objectives/Learning Outcomes/Capability Development

This course contributes to the program learning outcomes for the following programs: 

BP350 - Bachelor of Science (Statistics Major)  

PLO1 Apply a broad and coherent knowledge of scientific theories, principles, concepts and practice in one or more scientific disciplines.  
PLO2 Analyse and critically examine scientific evidence using methods, technical skills, tools and emerging technologies in a range of scientific activities. 
PLO3 Analyse and apply principles of scientific inquiry and critical evaluation to address real-world scientific challenges and inform evidence based decision making. 
PLO5 Work independently, with agility, safety, and accountability for own learning and professional future.  

BP340P23 - Bachelor of Data Science 
BP348 - Bachelor of Data Science (Professional)  

PLO 1 Knowledge - Apply a broad and coherent set of knowledge and skills for developing data driven solutions for contemporary societal challenges 
PLO 4 Communication - Communicate effectively with diverse audiences, employing a range of communication methods in interactions.to both computing and non computing personnel. 
PLO 5 Collaboration and Teamwork - Demonstrate effective teamwork and collaboration by using tools and practices to manage and meet project deliverables.  
PLO 6 Responsibility and Accountability - Demonstrate integrity, ethical conduct, sustainable and culturally inclusive professional standards, including First Nations knowledges and input in designing and implementing data driven solutions.  

BP083P23 - Bachelor of Applied Mathematics and Statistics (Statistics Major)  

PLO1 Apply a broad and coherent knowledge of mathematical and statistical theories, principles, concepts and practices with multi-disciplinary collaboration.  
PLO2 Analyse and critically examine the validity of mathematical and statistical arguments and evidence using methods, technical skills, tools and computational technologies.  
PLO3 Formulate and model real world problems using principles of mathematical and statistical inquiry to inform evidence-based decision making. 
PLO5 Work ethically and independently, with integrity and accountability to develop professional agility for future careers.  

BP083P23 - Bachelor of Applied Mathematics and Statistics (Mathematics Major)  

PLO4 Critically evaluate and communicate technical and non- technical mathematical and statistical knowledge to diverse audiences utilising a variety of formats employing culturally safe practices.  

For more information on the program learning outcomes for your program, please see the program guide. 


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

You 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.    

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course. 

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

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

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 

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.