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Curricular information is subject to change
1. Critically evaluate how data analytic tools and techniques can be applied to gain insights to data sets with consideration of the appropriate social contexts
2. Develop competency in data visualization and selected data analytics tools
3. Analyze reviews and assessment of published data analytics research within the local and global contexts
4. Apply written and oral communication of technical results through presentations and reports
5. Examine the ethical concerns surrounding data and critique the use of data with reference to current issues in a multicultural society
Some topics covered by this module include data visualisation, regression, classification, and clustering. The module will also discuss decision-making with data, emerging topics in data analytics, and ethical issues.
Student Effort Type | Hours |
---|---|
Lectures | 36 |
Specified Learning Activities | 40 |
Autonomous Student Learning | 50 |
Total | 126 |
Basic statistics and programming is recommended.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Exam (In-person): In-class Tests | Week 5, Week 10 | Alternative linear conversion grade scale 40% | No | 30 |
No |
Reflective Assignment: Discussion and journal (Brightspace/In-class) | Week 1, Week 4, Week 7, Week 9, Week 12 | Graded | No | 25 |
No |
Individual Project: Demonstrate analytical understanding and mastery of technical skills using a case study. Prelim Report Submission: Week 6 Final Report Submission: Week 12 |
Week 6, Week 12 | Graded | No | 30 |
No |
Participation in Learning Activities: Contribution to in-class activities. | Week 4, Week 8, Week 10 | Graded | No | 15 |
No |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities
Not yet recorded.