ACCT29085 - Financial Data Analytics

General Information

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
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Unit Availabilities from Term 3 - 2024

Term 1 - 2025 Profile
Jakarta
Term 2 - 2025 Profile
Jakarta

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).

Assessment Overview

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%).

Consult the University's Grades and Results Policy for more details of interim results and final grades

Past Exams

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Previous Feedback

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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: Students in class
Feedback
Students joined the MBA Data Science Major with the expectation that all of the topics are focused on Data science approach.
Recommendation
The topics should focus on data science approach where possible, and student expectations need to be managed where other topics are included.
Action Taken
All Financial Data Analytics topics in this unit focus on using data science approach
Source: Informal student feedback.
Feedback
One student tried to publish their assessment in an International Journal. But the journal in assessment submitted to CQU Moodle were also detected in turnitin process by the publisher so they got high plagiarism score.
Recommendation
In future unit offerings, the students will be made more aware of the fact that CQU uses Turnitin and that publishers also may use this, so they need to be aware of this issue if they wish to publish their assessment results.
Action Taken
In Progress
Unit learning Outcomes

On successful completion of this unit, you will be able to:

  1. Understand and distinguish alternative data analytics methods relevant to management decision making
  2. Apply data analytics to provide information for financial analysis, credit risk modeling and other applications using Numpy, Pandas and Matplotlib in Python
  3. Identify insights from financial data using machine learning approaches
  4. Apply visualization to reveal underlying data relationships using Tableau to inform decision making.


Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4
1 - Online Quiz(zes) • • • •
2 - Practical Assessment • •
3 - Project (applied) •
4 - Take Home Exam • • •
Alignment of Graduate Attributes to Learning Outcomes
• Professional Level
• Advanced Level
Graduate Attributes Learning Outcomes
1 2 3 4
1 - Knowledge • • • •
2 - Communication • • •
3 - Cognitive, technical and creative skills • • • •
Alignment of Assessment Tasks to Graduate Attributes
• Professional Level
• Advanced Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8