Last modified: 23 Jul 2024 11:06
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 | 3 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Offshore | Sustained Study | No |
| Co-ordinators |
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Content:
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Apply | Students will be able to manipulate, format, prepare, and clean data sets prior to analysis. |
| 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. |
| Procedural | Apply | Students will understand, and be able to use, basic data mining and visualization concepts, techniques and software tools. |
| Assessment Type | Summative | Weighting | 75 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Apply | Students will be able to manipulate, format, prepare, and clean data sets prior to analysis. |
| 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. |
| Procedural | Apply | Students will understand, and be able to use, basic data mining and visualization concepts, techniques and software tools. |
| Procedural | Evaluate | Students will be able to design appropriate visualization solutions for different applications, scenarios, and audiences. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 75 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Continuous assessment mark carried forward. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Continuous assessment mark carried forward. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| 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. |
| Procedural | Apply | Students will understand, and be able to use, basic data mining and visualization concepts, techniques and software tools. |
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