Last modified: 29 Aug 2025 13:46
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.
| Study Type | Undergraduate | Level | 4 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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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.
Content:
Obtaining, preparing, managing, and presenting data
Supervised learning, classification, regression
Unsupervised learning, clustering
Decision-tree learning
Neural networks and deep learning
Case-studies and applications
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 75 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
3,000-word individual report worth 75% of the overall grade. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Analyse | Ability to identify, prepare, and manage appropriate datasets for analysis. |
| Procedural | Create | Ability to appropriately present the results of data analysis |
| Procedural | Evaluate | Knowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done. |
| Procedural | Evaluate | Ability to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context. |
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Multiple-Choice Questions in-class test (1 hour) worth 25% of the overall grade. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Analyse | Ability to identify, prepare, and manage appropriate datasets for analysis. |
| Procedural | Create | Ability to appropriately present the results of data analysis |
| Procedural | Evaluate | Knowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done. |
| Procedural | Evaluate | Ability to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Resit Individual Report. Final grade will be best of either resit individual report, or resit individual report with carried-forward test marks. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Evaluate | Knowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done. |
| Procedural | Create | Ability to appropriately present the results of data analysis |
| Procedural | Evaluate | Ability to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context. |
| Procedural | Analyse | Ability to identify, prepare, and manage appropriate datasets for analysis. |
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