Last modified: 11 Aug 2025 12:16
This 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.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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• 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.
• Visualisation: information visualisation (basic concepts, advanced techniques such as treemaps); supporting user variation (abilities, knowledge, preferences)
• Applications to real world problems: for example, medical decision support, supporting analysis of genome data.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 50 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Word Count: 2400 (approx.) |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Analyse | Ability to identify appropriate goals for extracting information from different data sets, and to link data mining techniques to goals, applying this in practice. |
| Factual | Apply | Understanding of different types of data, and ability to prepare data sets for analysis using appropriate tools. |
| Procedural | Create | Ability to select, use, adapt and create appropriate tools, including computational tools, for data analysis and visualisation. |
| Procedural | Evaluate | Understanding of key models that support data mining, and ability to use appropriate models in practice. |
| Reflection | Analyse | Understanding of principles and techniques for visualisation and communication of data, and ability to apply these. |
| Assessment Type | Summative | Weighting | 50 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Word Count: 2400 (approx.) |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Analyse | Ability to identify appropriate goals for extracting information from different data sets, and to link data mining techniques to goals, applying this in practice. |
| Factual | Apply | Understanding of different types of data, and ability to prepare data sets for analysis using appropriate tools. |
| Procedural | Create | Ability to select, use, adapt and create appropriate tools, including computational tools, for data analysis and visualisation. |
| Procedural | Evaluate | Understanding of key models that support data mining, and ability to use appropriate models in practice. |
| Reflection | Analyse | Understanding of principles and techniques for visualisation and communication of data, and ability to apply these. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Factual | Apply | Understanding of different types of data, and ability to prepare data sets for analysis using appropriate tools. |
| Conceptual | Analyse | Ability to identify appropriate goals for extracting information from different data sets, and to link data mining techniques to goals, applying this in practice. |
| Procedural | Evaluate | Understanding of key models that support data mining, and ability to use appropriate models in practice. |
| Reflection | Analyse | Understanding of principles and techniques for visualisation and communication of data, and ability to apply these. |
| Procedural | Create | Ability to select, use, adapt and create appropriate tools, including computational tools, for data analysis and visualisation. |
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