All details in this unit profile for COIT20253 have been officially approved by CQUniversity and represent a learning partnership between the University and you (our student).
The information will not be changed unless absolutely necessary and any change will be clearly indicated by an approved correction
included in the profile.
Big data is a popular term used to describe the exponential growth and availability of structured and unstructured data. In this unit, you will explore big data within the context of business intelligence. In this unit, you will learn concepts of business intelligence, alignment of big data to business intelligence and how big data technologies can be used in building organisational business intelligence. You will learn how big data is changing businesses and how organisations can take advantage of big data in decision making. You will learn how organisations are integrating non-traditional unstructured data with the traditional structured enterprise data to do the business intelligence analysis. In order to understand these, you will learn big data analytical tools and technologies to help solve authentic business problems and make effective business decisions.
Career Level: Postgraduate
Unit Level: Level 9
Credit Points: 6
Student Contribution Band: 8
Fraction of Full-Time Student Load: 0.125
Pre-requisites or Co-requisites
Prerequisites: COIT20250 e-Business Systems, COIT20245 Introduction to Programming and COIT20247 Database Design and Development.
Anti-Requisites: If you have completed unit COIT20236 then you cannot take this unit.
Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit,
should drop the subsequent unit before the census date or within 10 working days of Fail grade notification.
Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability.
See details in the Assessment Policy and Procedure (Higher Education Coursework).
Offerings For Term 1 - 2022
All on-campus students are expected to attend scheduled classes –
in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory.
International students, on a student visa, must maintain a full time study load and meet
both attendance and academic progress requirements in each study period
(satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).
This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task,
based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%,
or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must
also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task,
as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%). Consult the
University’s Grades and Results Policy
for more details of interim results and final grades.
This list is not an exhaustive list of all University policies.
The full list of University policies are available on the CQUniversity Policy site.
Previous Student Feedback
Feedback, Recommendations and Responses
Every unit is reviewed for enhancement each year. At the most recent review,
the following staff and student feedback items were identified and recommendations were made.
Feedback from Student evaluation
Learning resources on big data technologies were insufficient.
Provide additional resources to support student learning.
Feedback from Student evaluation
Industrial case studies were inadequate.
Provide more real-life examples (industry cases) to enhance the student learning capability.
Unit Learning Outcomes
On successful completion of this unit, you will be able to:
Apply concepts and principles of big data to evaluate and explain how large volume of structured and unstructured data are managed in an organisation
Analyse critically and reflect on how organisations are including non-traditional valuable data with the traditional enterprise data to do the business intelligence analysis
Critically analyse and evaluate different big data technologies used for decision making in an organisation
Develop big data strategy for data-centric organisations to meet client requirements
Apply big data architecture, tools, and technologies for decision making and problem solving in the organisational context.
Australian Computer Society (ACS) recognises the Skills Framework for the Information Age (SFIA). SFIA is in use in over 100 countries and provides a widely used and consistent definition of ICT skills. SFIA is increasingly being used when developing job descriptions and role profiles.