In Progress
Please note that this Unit Profile is still in progress. The content below is subject to change.Overview
Big data is a popular term used to describe the exponential growth and availability of structured and unstructured data and business intelligence involves collecting, processing, analysing, and visualising data to help organisations make informed business decisions. In this unit, you will learn concepts of business intelligence, the alignment of big data with business intelligence, and how big data technologies can be leveraged to build organisational business intelligence. You will also explore contemporary tools in business intelligence and gain an understanding of data ethics, ensuring that data-driven solutions are developed and implemented responsibly and transparently. You will learn how to use big data for decision-making and impacting change in organisations. To understand these, you will be introduced to big data analytical tools and technologies to help solve authentic business problems and make effective business decisions. This unit provides a comprehensive foundation in big data and business intelligence with a strong business focus, equipping you with the skills needed for a successful career in data analytics along with expertise in big data strategy, architecture, and data ethics.
Details
Pre-requisites or Co-requisites
Prerequisites: COIT20250 Technologies in Information Systems Practice, and COIT20245 Introduction to Programming, and COIT20247 Database Design and Development. Anti-Requisites: COIT20236 Business Intelligence Management
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 - 2025
Attendance Requirements
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).
Recommended Student Time Commitment
Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.
Class Timetable
Assessment Overview
Assessment Grading
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.
All University policies are available on the CQUniversity Policy site.
You may wish to view these policies:
- Grades and Results Policy
- Assessment Policy and Procedure (Higher Education Coursework)
- Review of Grade Procedure
- Student Academic Integrity Policy and Procedure
- Monitoring Academic Progress (MAP) Policy and Procedure - Domestic Students
- Monitoring Academic Progress (MAP) Policy and Procedure - International Students
- Student Refund and Credit Balance Policy and Procedure
- Student Feedback - Compliments and Complaints Policy and Procedure
- Information and Communications Technology Acceptable Use Policy and Procedure
This list is not an exhaustive list of all University policies. The full list of University policies are available on the CQUniversity Policy site.
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 Unit and Teaching Evaluation
Most students rated the unit as Exceptional.
To continue with the good practices.
Feedback from ICT Course Committee
Aligning the unit with the latest SFIA 9 released.
To identify and integrate specific SFIA 9 skill categories relevant to the unit.
Feedback from Classroom Feedback
More hands-on exercises.
To add tutorial exercises based on the Spark ecosystem running in Google Colab, by referencing resources such as https://praxis-qr.github.io/BDSN/ ;https://colab.research.google.com/github/pnavaro/big-data/.
- 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 utilising non-traditional unstructured data with the traditional structured enterprise data to perform business intelligence analysis
- Evaluate and appraise different big data technologies used for decision making in an organisation
- Design a big data strategy for data-centric organisations that meets client requirements while addressing data ethics, ensuring responsible and transparent data usage throughout the process
- Explore big data architecture, tools, and technologies for decision making and problem solving in the organisational context.
The Australian Computer Society (ACS) recognises the Skills Framework for the Information Age (SFIA). SFIA is adopted by organisations, governments and individuals in many countries and provides a widely used and consistent definition of ICT skills. SFIA is increasingly being used when developing job descriptions and role profiles. ACS members can use the tool MySFIA to build a skills profile.
This unit contributes to the following workplace skills as defined by SFIA 9 (the SFIA code is included):
- Enterprise and Business architecture (STPL)
- Data Management (DATM)
- Business Intelligence (BINT)
- Data Analytics (DAAN)
- Artificial intelligence (AI) and data ethics (AIDE)
Alignment of Assessment Tasks to Learning Outcomes
| Assessment Tasks | Learning Outcomes | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1 - Written Assessment - 35% | |||||
| 2 - Presentation - 25% | |||||
| 3 - Project (applied) - 40% | |||||
Alignment of Graduate Attributes to Learning Outcomes
| Graduate Attributes | Learning Outcomes | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1 - Knowledge | |||||
| 2 - Communication | |||||
| 3 - Cognitive, technical and creative skills | |||||
| 4 - Research | |||||
| 5 - Self-management | |||||
| 6 - Ethical and Professional Responsibility | |||||
| 7 - Leadership | |||||
| 8 - Aboriginal and Torres Strait Islander Cultures | |||||