JC3503: Data Mining and Visualisation

JC3503: DATA MINING AND VISUALISATION (2023-2024)

Last modified: 23 Jul 2024 10:44

Course Overview

This course provides an introduction to machine learning and data mining. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time-series analysis, neural networks, and many other techniques. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.

Course Details

Study Type	Undergraduate	Level	3
Term	Second Term	Credit Points	15 credits (7.5 ECTS credits)
Campus	Offshore	Sustained Study	No
Co-ordinators	Dr Tryphon Lambrou

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

Any Undergraduate Programme (Studied)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

Course Description

Content:

Data Mining: basic statistics, advanced data analysis techniques such as trend detectors, pattern detectors, qualitative models, basic data mining techniques such as classification and clustering.
Visualization: information visualization (basic concepts and advanced techniques); supporting user variation (abilities, knowledge, preferences);
Applications to real world problems: for example, medical decision support, supporting analysis of genome data, text analysis

Contact Teaching Time

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

Teaching Breakdown

More Information about Week Numbers

Details, including assessments, may be subject to change until 21st September 2026 for Term 1 and Full Year courses and 8th January 2027 for Term 2 courses.

Summative Assessments

Computer Programming Exercise

Assessment Type	Summative	Weighting	25
Assessment Weeks		Feedback Weeks		Look up Week Numbers
Feedback

Learning Outcomes

Sorry, we don't have this information available just now. Please check the course guide on or with the Course Coordinator
Knowledge Level	Thinking Skill	Outcome

Exam

Assessment Type	Summative	Weighting	75
Assessment Weeks		Feedback Weeks		Look up Week Numbers
Feedback

Learning Outcomes

Sorry, we don't have this information available just now. Please check the course guide on or with the Course Coordinator
Knowledge Level	Thinking Skill	Outcome

Formative Assessment

There are no assessments for this course.

Resit Assessments

Computer Programming Exercise

Assessment Type	Summative	Weighting	25
Assessment Weeks		Feedback Weeks		Look up Week Numbers
Feedback	Continuous assessment mark carried forward.

Learning Outcomes

Sorry, we don't have this information available just now. Please check the course guide on or with the Course Coordinator
Knowledge Level	Thinking Skill	Outcome

Exam

Assessment Type	Summative	Weighting	75
Assessment Weeks		Feedback Weeks		Look up Week Numbers
Feedback	Continuous assessment mark carried forward.

Learning Outcomes

Sorry, we don't have this information available just now. Please check the course guide on or with the Course Coordinator
Knowledge Level	Thinking Skill	Outcome

Course Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Apply	Students will understand, and be able to use, basic data mining and visualization concepts, techniques and software tools.
Procedural	Analyse	Students will be able to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time series analysis, and many other techniques.
Conceptual	Apply	Students will be able to manipulate, format, prepare, and clean data sets prior to analysis.
Procedural	Evaluate	Students will be able to design appropriate visualization solutions for different applications, scenarios, and audiences.

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