ETC5521 · Diving deeply into data exploration

Overview

Beyond modelling and prediction, data might have many more stories to tell. Exploring data to uncover patterns and structures, involves both numerical and visual techniques designed to reveal interesting information that may be unexpected. However, an analyst must be cautious not to over-interpret apparent patterns, and to use randomisation tools to assess whether the patterns are real or spurious.

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. learn to use modern data exploration tools with many different types of contemporary data to uncover interesting structures, unusual relationships and anomalies
2. understand how to map out appropriate analyses for a given data set and description, define what we would expect to see in the data, and whether what we see is contrary to expectations
3. be able to compute null samples in order to test apparent patterns, and to interpret the results using computational methods for statistical inference
4. critically assess the strength and adequacy of data analysis.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

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