The topic of the unit is poorly addressed at Monash. It is an area seeing growing demand in the Data Science community, so much so that some students have mentioned it in informal discussions. It is also an area where Monash faculty distinguish themselves internationally, and thus we can draw on quality material and guidance in preparation of the unit. Finally, the MDS is clearly in need of additional data analysis units.

24/9/2019: Admin - adding 30 minutes reading time to the overall exam duration as per University requirements.

3/2/20: Admin - amendment to the exam reading and noting time. Reducing from 30mins to 10mins (2hrs 10 mins total) to meet University standards.

18/09/2020 - Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

13/11/2020 - Wray Buntine: change laboratory to tutorial to reflect mode of class

Updated - added online offering assessments

This unit will be offered as an elective in the C6004 Master of Data Science.

At the completion of this unit, students should be able to:

1. appraise what kinds of semi-structured data exist and the problems they present for analysis;
2. analyse different kinds of algorithms for different kinds of semi-structured data;
3. develop and modify some standard algorithms for semi-structured data;
4. examine some characteristic industry problems involving semi-structured data, and analyse the suitability of different algorithms.

020119

Semi-structured data is one of the fastest growing kinds of data in both the public and private sector, for instance in health. Email collections with sender-recipient graphs, metadata and text content is one example. This unit will explore basic forms of semi-structured data: text, time-sequence data, graphs and multiple relations in a database. Basic machine learning algorithms for these kinds of data will be analysed and applied. Some characteristic industry problems for the application of semi-structured data will also be investigated such as cohort analysis and market-basket analysis.

Technological requirements

Students will be using Python and Jupyter Notebook for assignments and tutorials. Students are recommended to bring their own laptop for these.

On-campus

Examination (2 hours and 10 minutes): 50%; in-semester assessment: 50%.

1. Assessment 1: Text Analysis - 25% - ULO: 1, 2, 3
2. Assessment 2: Graph data analysis - 25% - ULO: 2, 3, 4
3. Examination - 50% - ULO: 1, 2, 3, 4

Minimum total expected workload equals 12 hours per week comprising:

A minimum of 8 hours per week of personal study (22 hours per week for Monash Online students) for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

FIT

FIT5197