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CIS8025 Big Data Analytics

Semester 2, 2019 Online
Short Description: Big Data Analytics
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


Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.


Data has become an important resource both in and for organisations. Many thousands of terabytes of data come into organisations on a daily basis through transactional data, sensor networks and social media. Managers in the IT profession have the responsibility of communicating this data to a variety of stakeholders. Using computing power and graphic tools it is possible to convert big data into visual forms for quick and easy comprehension so that evidence can drive the decision-making process within an organisation.


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 fundamental and advanced aspects of visualisation design and strategies as well as techniques for creating user-oriented visualisation designs using a range of tools. Students will explore, through a hands-on project, elements of visualisation design, and strong policy for privacy and security of citizens' data.


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

  1. Synthesize 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. Analyse 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.
  7. Make ethical decisions in regards to privacy and security in Big Data Visualisation projects.


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
Open Data VIs MGT Report 100 30 02 Sep 2019
Data Decision-making Report 100 50 14 Oct 2019

Important assessment information

  1. Attendance requirements:
    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.

  2. Requirements for students to complete each assessment item satisfactorily:
    To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks. (Depending upon the requirements in Statement 4 below, students may not have to satisfactorily complete each assessment item to receive a passing grade in this course.)

  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 achieve at least 50% of the total weighted marks available for the course.

  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 items for the course.

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

  7. Examination period when Deferred/Supplementary examinations will be held:
    Not applicable.

  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. Referencing in assignments must comply with the Harvard (AGPS) referencing system. This system should be used by students to format details of the information sources they have cited in their work. The Harvard (APGS) style to be used is defined by the USQ library’s referencing guide. This guide can be found at