Credit Points 10

Legacy Code 300958

Coordinator Weicong Li Opens in new window

Description The Social Web provides everyone with a voice; information from Facebook, Twitter and other social networks allows us to identify trends and relationships in society. Whilst this has interest on a personal level, the killer-apps will be in analysing social Web data for business, such as tracking the buzz around a new product, and understanding the relationships between customers and products. This subject will introduce its students to the Social Web data that is available, and blend data science and machine learning concepts to allow extraction and analysis of such data.

School Computer, Data & Math Sciences

Discipline Statistics

Student Contribution Band HECS Band 1 10cp

Check your fees via the Fees page.

Level Undergraduate Level 3 subject

Pre-requisite(s) Students NOT enrolled in the 3770 must have successfully completed one of the following groupings
(MATH 1028 Statistical Decision Making OR
MATH 1003 Biometry OR
COMP 1014 Thinking about Data)
OR
(MATH 1030 Statistics for Business AND
COMP 1013 MATH 1002 Analytics Programming)
OR
(MATH 1012 Management Analytics AND
COMP 1013 MATH 1002 Analytics Programming)
OR
(MATH 2006 Experimental Design and Analysis AND
COMP 1013 MATH 1002 Analytics Programming)

Co-requisite(s) For students enrolled in courses 3769 Bachelor of Data Science or 3770 Bachelor of Applied Data Science
COMP 1014 Thinking About Data

Assumed Knowledge

Students are expected to be familiar with fundamental computer programming concepts and required to have a prior statistics knowledge.

Learning Outcomes

On successful completion of this subject, students should be able to:

1. extract and process formatted data from social Web sources.
2. use computer algorithms to visualise complex social Web interactions.
3. use mathematical and statistical methods to identify significant trends in the social Web.
4. use mathematical and statistical techniques to identify critical regions of a social network.
5. partition a social network into clusters.
6. choose an appropriate metric to measure the interaction between social network nodes.
7. compute the popularity, authority and hub scores for network nodes.

Subject Content

Data extraction and formatting.
Visualisation of social networks.
Identifying trends in social networks.
Measuring similarity in multiple networks.
Clustering social network information
Finding authorities and hubs in a social network.