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CIS8701 Big Data Visualisation

Semester 1, 2019 Online
Short Description: Big Data Visualisation
Units : 1
Faculty or Section : Faculty of Business, Education, Law and Arts
School or Department : School of Management and Enterprise
Student contribution band : Band 2
ASCED code : 020300 - Information Systems
Grading basis : Graded


Examiner: Sophie Cockcroft

Other requisites

Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at


The rate of change and direction of Information Technology (IT) represents both challenges and opportunities for business managers. In recent years, thousands of terabytes of data are created on a daily basis. As future managers, we need to understand how these data are ‘seen’ in various forms so that decision making is facilitated in organisations. Using computing power and graphic tools, now it is possible to convert these big data into a form of visuals for easy and quick comprehension. It is imperative that practitioners, decision makers and managers learn the skills to visualise big data, analyse trends, and comprehend best practice approaches so that big data can be presented in a range of forms that best support decision-making in a range of organisations.


This course provides students with best practice principles in big data visualisation design and skills for the development of visuals that synthesise big data in ways that inform decision making for a range of organisations. The course provides both foundation and advanced aspects of visualisation design and strategies as well as techniques for creating user oriented visualisation designs using a range of tools.


On successful completion of this course, students will be able to:

  1. demonstrate academic and professional knowledge of recent developments in big data visualisation design principles
  2. evaluate relationships between big data and visualisation design concepts
  3. explore and apply big data conversion into visual forms for decision-making purposes
  4. critique innovative visualisation approaches to provide solutions to real-world problems
  5. identify potential opportunities for creative and sustainable use of big data visualisation to achieve corporate objectives
  6. use big data visualisation to communicate information to specialist and non-specialist audiences.


Description Weighting(%)
1. Investigation of visualisation design 25.00
2. Big data visualisation approaches 25.00
3. Big data visualisation issues 20.00
4. Implementations of big data visualisation using tools 20.00
5. Impact of big data visualisation on business decision-making. 10.00

Text and materials required to be purchased or accessed

ALL textbooks and materials available to be purchased can be sourced from USQ's Online Bookshop (unless otherwise stated). (

Please contact us for alternative purchase options from USQ Bookshop. (

There are no texts or materials required for this course.

Reference materials

Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.

Student workload expectations

Activity Hours
Assessments 60.00
Lectures 20.00
Private Study 60.00
Tutorials 30.00

Assessment details

Description Marks out of Wtg (%) Due Date Notes

Important assessment information

  1. Attendance requirements:
    Online: If you are an international student in Australia, you are advised to attend all classes at your campus. For all other students, there are no attendance requirements for this course. However, it is the students' responsibility to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

    On-campus: It is the students' responsibility to attend and participate appropriately in all activities (such as lectures, tutorials, laboratories and practical work) scheduled for them, and to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

  2. Requirements for students to complete each assessment item satisfactorily:
    Not applicable.

  3. Penalties for late submission of required work:
    Students should refer to the Assessment Procedure (point 4.2.4)

  4. Requirements for student to be awarded a passing grade in the course:
    To be assured of receiving a passing grade a student must submit a completed project which demonstrates a satisfactory level of achievement in all essential objectives.

  5. Method used to combine assessment results to attain final grade:
    The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative assessment items in the course.

  6. Examination information:
    There is no examination in this course.

  7. Examination period when Deferred/Supplementary examinations will be held:
    There is no examination in this course, there will be no deferred or supplementary examinations.

  8. University Student Policies:
    Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at

Assessment notes

  1. Students will be advised regarding the referencing style to be used to format details of the information sources they have cited in their work. The referencing styles used are defined by the USQ Library's referencing guide at