Last modified: 28 Jun 2018 10:27
course aims to make students familiar with basic data mining and visualisation techniques
and software tools. Students will learn how to analyse complex
datasets by applying data pre-processing, exploration, clustering and classification,
time series analysis, and many other techniques. This course will also cover
text mining and qualitative modelling. Through this course students will be
able to analyse real-world datasets in various domains and discover novel
patterns from them. This course is particularly suitable for those who are
interested in working as data analysts or data scientists in the future.
|Session||First Sub Session||Credit Points||15 credits (7.5 ECTS credits)|
Information on contact teaching time is available from the course guide.
1st Attempt: 1 two-hour written examination (75%); continuous assessment (25%).
Resit: One 2-hour examination (100%). Only the marks obtained at first attempt can be used for Honours classification.
During lectures, the Personal Response System and/or other ways of student interaction will be used for formative assessment. Additionally, practical sessions will provide students with practice opportunities and formative assessment.