Unit Synopsis
As the economy moves towards more digital disruption, management are seeking innovative technologies for generating insights for decision making. The unit is designed to provide you with an understanding of how financial data of an organisation can be analysed for insights using data analytics. You are introduced to concepts, tools, software and methodologies of data science and how they are applied to the analysis of financial data. You will gain experience in analysing transaction data and financial ratios for segmentation, credit data for risk modelling, next best product offer, visualising data, and generating dashboards for performance reporting. This unit is suitable for students with minimal business, finance and information systems background.
Details
| Level | Postgraduate |
|---|---|
| Unit Level | 9 |
| Credit Points | 6 |
| Student Contribution Band | SCA Band 4 |
| Fraction of Full-Time Student Load | 0.125 |
| Pre-requisites or Co-requisites |
Pre-requisite: ACCT28002 Accounting for Management Decision Making Co-requisite: ACCT28003 Business Analytics Techniques. Students enrolling in this unit must be undertaking the CL84 Master of Business Administration (International).
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). |
| Class Timetable | View Unit Timetable |
| Residential School |
Optional Residential School View Unit Residential School |
Unit Availabilities from Term 1 - 2026
Term 2 - 2026 Profile
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.
Assessment Tasks
| Assessment Task | Weighting |
|---|---|
| 1. Online Quiz(zes) | 20% |
| 2. Practical Assessment | 20% |
| 3. Project (applied) | 30% |
| 4. Take Home Exam | 30% |
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%).
Past Exams
All University policies are available on the Policy web site, however you may wish to directly view the following policies below.
This list is not an exhaustive list of all University policies. The full list of policies are available on the Policy web site .
Term 1 - 2025 : The overall satisfaction for students in the last offering of this course was 100.00% (`Agree` and `Strongly Agree` responses), based on a 42.86% response rate.
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.
Source: Informal student feedback to the Head of Course
Incorporation of latest industry trends (e.g. Big Data) is important in a unit like this and should be continued.
Such content is already included in the unit and this approach will be retained, while new developments will be incorporate as and when they emerge.
Additional content has been added to the unit in line with feedback.
Source: Students were complimentary about this aspect and provided informal feedback to their lecturer.
The content is very practical and focused on business application of data analytics.
This approach will be continued in the forthcoming term.
In Progress
On successful completion of this unit, you will be able to:
- Understand and distinguish alternative data analytics methods relevant to management decision making
- Apply data analytics to provide information for financial analysis, credit risk modeling and other applications using Numpy, Pandas and Matplotlib in Python
- Identify insights from financial data using machine learning approaches
- Apply visualization to reveal underlying data relationships using Tableau to inform decision making.
| Assessment Tasks | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Online Quiz(zes) | • | • | • | • |
| 2 - Practical Assessment | • | • | ||
| 3 - Project (applied) | • | |||
| 4 - Take Home Exam | • | • | • | |
| Graduate Attributes | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Knowledge | • | • | • | • |
| 2 - Communication | • | • | • | |
| 3 - Cognitive, technical and creative skills | • | • | • | • |
| Assessment Tasks | Graduate Attributes | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 8 | |