Course coordinator
Dr Ali Darvishi
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Data mining techniques enable the identification of patterns and relationships in a variety of data types. Identifying previously unknown patterns and relationships in data can help business make better decisions through, for example, identifying new trends, predicting customer loyalty, supporting targeted marketing campaigns. This course introduces various data mining techniques and tools, and teaches students to apply them to various business problems across several different types of data.
This course explores the complexities of business analytics with a focus on the three domains of Audit, Healthcare, and Human Resources.
Adopting a problem-based approach to learning, students explore a variety of theories, tools and techniques, applying their skills using rich data sets and sources.
Taught by an experienced academic team, the course incorporates contemporary industry practices and insights, and features guest speakers, presentations and podcasts.
This course offers a fully online student experience that engages students through interactive content within a dedicated learning platform. To enhance engagement, a series of 'live sessions' are incorporated with discussions encouraged on a social platform. All live sessions are recorded and accessible to students, allowing for flexible learning. This course maintains a strong focus on practical learning, enabling students to apply their business analytic and domain knowledge effectively. Authentic assessments are used to test student abilities to translate and apply skills across real world scenarios, enhancing student employability whilst incrementally adding value to their current or future employers.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BSAN7205 + 7209 + 7210 + 7212
Restrictions
MBusAn
Course contact
Course staff
Lecturer
Dr Ali Darvishi
Dr Katie Williams
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full Name
- Student ID
- Course Code
Aims and outcomes
This course aims to draw on, develop and apply analytics capabilities that students have gained in the program, across the domains of Health Care, Human Resources, and Audit to solve complex business problems, leveraging diverse and rich data assets.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Identify and apply analytics across a range of emerging business domains.
LO2.
Evaluate appropriate analytic techniques that can be used within different applied business domains.
LO3.
Communicate analytic insights effectively to diverse stakeholders.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project | A1: Design a Continuous Auditing Model | 30% |
23/08/2024 5:00 pm |
| Product/ Design | A2: Design a Machine Learning Model | 30% |
13/09/2024 5:00 pm |
| Paper/ Report/ Annotation | A3: HR Data Analysis and Report | 30% |
18/10/2024 5:00 pm |
| Essay/ Critique, Reflection | A4: Reflective Essay | 10% |
8/11/2024 5:00 pm |
Assessment details
A1: Design a Continuous Auditing Model
- Mode
- Written
- Category
- Project
- Weight
- 30%
- Due date
23/08/2024 5:00 pm
- Learning outcomes
- L01, L02, L03
Task description
The task in this assessment is to design, describe and apply a continuous auditing model, based on your reading and understanding of the details presented in the DBS Bank case study “Innovation in Assurance: Doing More, and More Effectively, with Less”.
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task; however, students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
To be submitted via Blackboard
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
A2: Design a Machine Learning Model
- Mode
- Product/ Artefact/ Multimedia
- Category
- Product/ Design
- Weight
- 30%
- Due date
13/09/2024 5:00 pm
- Learning outcomes
- L01, L02, L03
Task description
In this assignment, students have the option to design and develop a new machine learning model or utilise and adapt the model that was previously recommended in module 2.2.
The key task is to choose and endorse just one model, providing justification for why it is the recommended model with respect to three critical areas of healthcare analytics: operational management, cost management, and care management.
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task; however, students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
To be submitted via Blackboard
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
A3: HR Data Analysis and Report
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
18/10/2024 5:00 pm
- Learning outcomes
- L01, L02, L03
Task description
The key task in this assessment is to provide insights into the features that explains or may be associated with turnover patterns of +10,000 strong group of employees using the data set provided.
You will need to identify an interesting finding within your analytics and develop a 1000-word (maximum) report on findings.
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task; however, students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
To be submitted via Blackboard
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
A4: Reflective Essay
- Mode
- Written
- Category
- Essay/ Critique, Reflection
- Weight
- 10%
- Due date
8/11/2024 5:00 pm
- Learning outcomes
- L02
Task description
For this assessment, you will need to write an essay reflecting on your learning experience across this course, applying the four components of reflective thinking, adapted from The Integrated Reflective Cycle (Bassot, 2013).
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task; however, students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submissions independent of AI tools.
Submission guidelines
To be submitted via Blackboard
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Not Timetabled |
Module 1: Audit Analytics | Sub-Module 1.1 Self-Directed Learning: Foundations of Audit Analytics. This topic identifies and explains the key principles of audit and assurance, explores the purposes of data analytics in the field of audit, and introduces the audit decision-making framework. Learning outcomes: L01, L02 |
Week 2 |
Not Timetabled |
Module 1: Audit Analytics | Sub-Module 1.2 Self-Directed Learning: Audit Analytic Techniques. This topic focuses on the alignment between analytic tools and techniques, and the audit engagement. Learning outcomes: L01, L02, L03 |
Seminar |
Module 1: Audit Analytics | Sub-Module 1.3 Self-Directed Learning: Machine Learning and AI in the Audit Domain. This topic explores Machine Learning (ML) and Artificial Intelligence (AI) in the field of audit analytics, placing specific focus on the Continuous Auditing model. Learning outcomes: L01, L02, L03 |
|
Week 3 |
Seminar |
Live Session | Audit Analytics Learning outcomes: L01, L02 |
Week 4 |
Not Timetabled |
Module 1: Reflection Reflect on course materials for Module 1 and finalise Assessment 1. Learning outcomes: L01, L02, L03 |
Week 5 |
Not Timetabled |
Module 2: Healthcare Analytics | Sub-Module 2.1 Self-Directed Learning: Transformation in the Healthcare Domain. This topic focuses on the application of data analysis techniques in healthcare. You will explore the Healthcare Analytics Framework and review a variety of case studies featuring the application of bespoke analytics to solve healthcare related challenges. Learning outcomes: L01, L02 |
Week 6 |
Not Timetabled |
Module 2: Healthcare Analytics | Sub-Module 2.2 Self-Directed Learning: Advanced Analytics in Healthcare. This topic introduces both basic and advanced sources of data and analysis techniques commonly used in healthcare. Learning outcomes: L01, L02, L03 |
Seminar |
Live Session | Healthcare Analytics Learning outcomes: L01, L02 |
|
Week 7 |
Not Timetabled |
Module 2: Healthcare Analytics | Sub-Module 2.3 Self-Directed Learning: Implementing Healthcare Analytics. This topic focuses on challenges and crucial decisions that a healthcare data analyst may face while developing and implementing a real-world healthcare analytics project. Learning outcomes: L01, L02, L03 |
Week 8 |
Seminar |
Module 2: Reflection Reflect on course materials for Module 2 and finalise Assessment 2. Learning outcomes: L01, L02, L03 |
Week 9 |
Not Timetabled |
Module 3: HR Analytics | Sub-Module 3.1 Self-Directed Learning: The Three Core Pillars of HR Analytics. This topic introduces the concepts of the Human Resources (HR) function, Human Resource Management (HRM) and the three pillars of HR Analytics. The first pillar of HR Analytics, Engagement, is explored in detail. Learning outcomes: L01, L02 |
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Not Timetabled |
Module 3: HR Analytics | Sub-Module 3.2 Self-Directed Learning: Employee Performance. This topic explores the second core pillar of HR analytics, employee performance. An industry case snapshot is also provided, which will play a central role in your third assessment task. Learning outcomes: L01, L02, L03 |
Seminar |
Live Session | HR Analytics Learning outcomes: L01, L02, L03 |
|
Week 11 |
Not Timetabled |
Module 3: HR Analytics | Sub-Module 3.3 Self-Directed Learning: Employee Turnover. This topic focuses on the third pillar of HR Analytics, employee turnover. Learning outcomes: L01, L02, L03 |
Week 12 |
Not Timetabled |
Module 4: Reflection Self-Directed Learning: Fostering Action Through Integration. In this concluding module, we will bring together the knowledge you have gained from the three domains. Learning outcomes: L01, L02, L03 |
General contact hours |
Drop in consultations available |
|
Week 13 |
Seminar |
Modules 3 and 4: Reflection Reflect on course materials and finalise Assessment 3, progress Assessment 4. Learning outcomes: L01, L02, L03 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.