220700 Data Driven Decision Making
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksThere are course requisites for this subject. See access conditions.
Description
With the advancement of technology, the amount of data generated globally continues to grow enormously. Knowing how to utilise this data to make evidence-based decisions is vital for leaders of tomorrow. This subject provides learners with the tools and understanding necessary to use data in making evidence-based decisions as well as the ethical considerations in regard to the collection, storage and use of data. Learners learn about different types of analyses, predictive models and fact-based management to create value for organisations.
Subject learning objectives (SLOs)
1. | Understand the role of data analytics for an organisation |
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2. | Demonstrate methods to locate, prepare and analyse data in order to identify business problems and make predictions |
3. | Communicate the results of a data analytics project |
4. | Review ethical principles as they relate to the collection, storage and use of data by organisations |
Contribution to the development of graduate attributes
This subject contributes to the development of the following graduate attribute(s):
- Intellectual rigour and innovative problem solving
- Communication and collaboration
This subject also contributes specifically to develop the following Program Learning Objective(s) in the Master of Business Administration:
- Apply critical thinking and advanced analytical skills to develop innovative solutions that address strategic business issues in complex contexts (1.1)
- Communicate ideas, decisions and strategies clearly and apply interpersonal skills that are sensitive to Indigenous peoples and other diverse peoples, cultures and contexts (2.1)
Teaching and learning strategies
This subject is delivered completely online with a number of live online sessions to discuss any issues in relation to content and assessment tasks. Students are expected to complete a range of different learning activities throughout the week. Activities provide opportunities to learn, apply and discuss the knowledge gained in a practical manner. Feedback is provided from both peers and teaching staff throughout the various activities. You are encouraged to actively provide feedback and interact with staff and students.
Content (topics)
- Introduction to data analytics
- Data exploration and predictive modelling
- Ethical considerations in relation to data
- Communication and presentation
Assessment
Assessment task 1: Context and Ideation (Individual)
Objective(s): | This addresses subject learning objective(s): 1, 2 and 4 |
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Weight: | 30% |
Length: | 750 words, excluding references |
Criteria: |
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Assessment task 2: Data Analytics Report (Individual)
Objective(s): | This addresses subject learning objective(s): 2 and 3 |
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Weight: | 40% |
Length: | 1000 words, excluding references |
Criteria: |
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Assessment task 3: Data Analytics Presentation (Individual)*
Objective(s): | This addresses subject learning objective(s): 1, 2, 3 and 4 |
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Weight: | 30% |
Length: | 6 - 8 minute video presentation |
Criteria: |
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Minimum requirements
Learners must achieve at least 50% of the subject’s total marks.
Required texts
There is no prescribed textbook required.
References
Resources from various sources will be used throughout the course.