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Last modified: 22 May 2019 17:07

Course Overview

This course will provide students of our MSc in AI with knowledge of core data mining and visualisation approaches, tools, techniques and technologies. The students will be enhanced with data science skills for their future career.

Course Details

Study Type Postgraduate Level 5
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Old Aberdeen Sustained Study No
  • Dr Wei Pang
  • Dr Chenghua Lin

What courses & programmes must have been taken before this course?

  • Any Postgraduate Programme (Studied)

What other courses must be taken with this course?


What courses cannot be taken with this course?


Are there a limited number of places available?


Course Description

This course aims to make students familiar with 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.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 10 Lectures during University weeks 25 - 26
  • 5 Practicals during University weeks 25 - 26

More Information about Week Numbers

Summative Assessments

Individual Project (50%); Individual Project (50%).

Resit: where a student fails the course overall they will be afforded the opportunity to resit those parts of the course that they failed (pass marks will be carried forward).


Formative Assessment

There are no assessments for this course.


Formative feedback for in-course assessments will be provided in written form. Additionally, formative feedback on performance will be provided informally during practical sessions.

Course Learning Outcomes


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