ITO5212 · Data analysis for semi-structured data

Overview

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.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

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.

Workload

A minimum of 144 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.

Student experience, not official rules. Nothing here changes what the Handbook says.