Course Title: Data Visualization and Communication

Part A: Course Overview

Course Title: Data Visualization and Communication

Credit Points: 12.00

Course Code




Learning Mode

Teaching Period(s)

Course Coordinator: Associate Professor James Thom

Course Coordinator Phone: +61 3 9925 2992

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-requisite:  Successful completion of the course: COSC2670 Practical Data Science. 


Note it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For your information the RMIT Course Requisites policy can be found at Course requisites –;ID=twx09y07zi1c 


Course Description

Data visualisation is a way to explore and understand the underlying data, and is used to convey or reveal data, compare parts of the data or different variables in analysis, depict the data characteristics or trends at different levels. Data visualisation is an essential part of data science and analytics across all phases of the data science lifecycle: data preparation, initial explorations, pre-processing, communicating or revealing findings from data, and providing data-driven evidence in decision making.

Communicating clearly and effectively about the patterns you find in data is a key skill for a successful data scientist. Visualizations are graphical depictions that can improve comprehension. Collaborative filtering visualizations will be paired with verbal analyses and reporting.

In this course, you will learn about the different ways of visualising data, and learn to visualise data differently, given the data type/characteristics and the specific analysis techniques required for the data. You will learn to assess and evaluate existing data visualisation techniques and develop your own on top of them. In particular, you will

  • apply different tools to transform data and create visualizations, including Python, Google Charts, Tableau, and Spotfire, and
  • learn to develop an end-to-end interactive visual analytics platform, which combines data processing and data presentation for users to explore and learn about the data.

Assignments will give you experience with reporting on complex patterns and results with graphics and prose.    

Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the following Program Learning Outcomes for MC267Master of Data Science:

Enabling Knowledge: You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will:

  • Demonstrate mastery of a body of knowledge that includes recent developments in computer science and information technology;
  • Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools;
  • Recognise and use research principles and methods applicable to data science.

Critical Analysis: You will learn to accurately and objectively examine, and critically investigate computer science, information technology (IT) and statistical concepts, evidence, theories or situations, in particular to:

  • Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems;
  • Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements;
  • Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of statistical problems.

Problem Solving: Your capability to analyse complex problems and synthesise suitable solutions will be extended as you learn to:

  • Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification;
  • Apply an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Communication: You will learn to communicate effectively with a variety of audiences through a range of modes and media, in particular to:

  • Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.

Team Work: You will learn to work as an effective and productive team member in a range of professional and social situations, in particular to:

  • Work effectively in different roles, to form, manage, and successfully produce outcomes from collaborative teams, whose members may have diverse cultural backgrounds and life circumstances, and differing levels of technical expertise.

Course Learning Outcomes

On successful completion of this course you should be able to:

  1. Differentiate between alternative ways of visualising data
  2. Choose the most appropriate methods given the data type/characteristics and the specific analysis techniques required to explain the data
  3. Develop an end-to-end interactive visual analytics platform, which combines data processing and data presentation for users to explore and learn about given data
  4. Perform critical analysis of problems and insights hidden in data through research and experiments with various datasets.
  5. Articulate and present the output of visualization and visual analytics to a range of stakeholders. 
  6. Work in teams and engage in team-based research and development. 

Overview of Learning Activities

You will engage with key concepts in lectures, classes or online, where course material will be presented and the subject matter will be illustrated with demonstrations and examples.

Tutorials, workshops and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide you with practice in the application of theory and procedures. You will explore concepts with teaching staff and other students, and receive feedback on your progress. You will develop an integrated understanding of the subject matter through private study by working through the course as presented in classes. Comprehensive learning materials will aid you in gaining practice at solving conceptual and technical problems.    


This course includes 2 hours per week of lectures and 2 hours per week of tutorial/laboratory classes. To achieve high levels of academic results you are expected to spend on average an additional 6 hours per week on self-directed independent learning (reading, online activities and assignments).    

Overview of Learning Resources

You will make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through myRMIT and may be provided with copies of additional materials in class or via email.

Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.    

Overview of Assessment

Note: This course has no hurdle requirements.

The assessment for this course comprises practical work involving the development and use of computer programs to visualise date, and a final exam. For standard assessment details, relating to Computer Science and IT courses see:


Assessment tasks

Early Assessment Task: Practical Assignment 

Weighting 15%

This assessment task supports CLOs 1, 2 & 4

Assessment Task 2: Practical Project and presentation

Weighting 35%

This assessment task supports CLOs 1, 2, 3, 4, 5 & 6

Assessment 3: Exam

Weighting 50% 

This assessment supports CLOs 1, 2 & 3