MGMT29005 - Data and Ethics

General Information

Unit Synopsis

This advanced unit builds on the ethical and legal foundations introduced in the core units LAWS20063 Governance and Business Law and MGMT20130 Operations Management and Business Analytics. The emerging field of data science encompasses "big data" and "data analytics". In this unit you will analyse ethical considerations specific to study design, data collection methods, data analysis and the appropriate dissemination and application of findings. You will apply the ethical duties of researchers and analysts to ensure that ethical protocols have been respected and that the rights and consequences of participants and users have been acknowledged and respected. You will employ practical tools to help you to identify ethical dilemmas and develop strategies for ensuring ethical decision making and resulting behaviours. You will also examine the role of organisational and industry cultures in shaping ethical (or unethical) data analytic practices, thus addressing the United Nation's sustainability development goals with respect to responsible business operations, new business models, investment, innovation and technology and collaboration.

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

Students enrolling in this unit must be undertaking the CL84 Master of Business Administration (International) or the CM45 Professional Certificate in Business (Data Science).

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 No Residential School

Unit Availabilities from Term 3 - 2024

Term 1 - 2025 Profile
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. Written Assessment 40%
2. Presentation 30%
3. Reflective Practice Assignment 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

To view Past Exams,
please login
Previous Feedback

No previous feedback available

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: Self-reflection
Feedback
Need more discussion among students
Recommendation
Implement more interactive workshop activities to encourage students to participate in discussions.
Action Taken
Questions asked during the session, pause, and encourage them to make comments or feedback before continuing to the next topics. One or two students participated and made good comments.
Source: Self-reflection
Feedback
Need more collection of available 'case studies'
Recommendation
Case studies (e.g. Harvard style) on the unit theme would be a good source for reference as well as for the final exam or group assessment.
Action Taken
This is an ongoing activity - some inroads have been made but more cases still need to be identified.
Source: Self-reflection.
Feedback
The group assignment should have an individual component.
Recommendation
Incorporate an individual component within the group assignment, based on students' self- and peer-assessment of their group mates.
Action Taken
In Progress
Unit learning Outcomes

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

  1. Critically reflect on the ethical dimensions of the data science, its purpose, methods and impact in data-driven organisations
  2. Apply ethical techniques in auditing data-driven processes in organisational contexts
  3. Analyse established ethical techniques and strategies, independently and within teams, to identify and minimize potential harm associated with data driven organisational processes
  4. Demonstrate knowledge, skills and ideas related to ethical aspects of data, its collection, management and uses to a range of stakeholders.

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4
1 - Written Assessment • • • •
2 - Presentation • •
3 - Reflective Practice Assignment • •
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 • •
4 - Research • • •
5 - Self-management • •
6 - Ethical and Professional Responsibility • • •
7 - Leadership •
Alignment of Assessment Tasks to Graduate Attributes
• Professional Level
• Advanced Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8