Digital Methods
Subject MECM90028 (2016)
Note: This is an archived Handbook entry from 2016.
Credit Points: | 6.25 | ||||||||||||
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Level: | 9 (Graduate/Postgraduate) | ||||||||||||
Dates & Locations: | This subject has the following teaching availabilities in 2016: February, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here. | ||||||||||||
Time Commitment: | Contact Hours: 12 hours – 4 x 3 hour seminars (2 per teaching day, over 2 days) Total Time Commitment: 85 hours | ||||||||||||
Prerequisites: | Admission into 101AA Ph.D.- Arts or DR-PHILART Doctor of Philosophy in Arts. | ||||||||||||
Corequisites: | None | ||||||||||||
Recommended Background Knowledge: | None | ||||||||||||
Non Allowed Subjects: | None | ||||||||||||
Core Participation Requirements: |
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry. It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability |
Subject Overview: |
This skills-led course is taught in computer labs. Students will learn how to collect website and social media data using both screen-scraping techniques and through APIs. The course is designed for non-programmers; no coding skills will be required. Once collected, students will learn how to clean and then analyse the data using two methods: content analysis and social network analysis. Finally, the course will introduce students to data visualisation techniques. |
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Learning Outcomes: |
On successful completion of this subject, students should have:
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Assessment: |
1. One 2,500-word essay (100%), due 2 weeks after the end of the teaching period. Hurdle Requirements: Students are required to attend a minimum of 100% of classes in order to pass this subject. |
Prescribed Texts: | None |
Breadth Options: | This subject is not available as a breadth subject. |
Fees Information: | Subject EFTSL, Level, Discipline & Census Date |
Generic Skills: |
This subject will contribute, through teaching and discussion with academic staff and peers, to developing skills and capacities including those identified in the University-defined Graduate Attributes for the PhD, in particular:
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Links to further information: | http://arts.unimelb.edu.au/graduate-studies/research |
Related Course(s): |
Doctor of Philosophy - Arts |
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