Last modified: 10 Oct 2025 12:16
This course provides an introduction to machine learning, data mining, and data visualisation. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, regression, classification, clustering, time-series analysis, text mining, 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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In this course, you will learn the skills to analyse, visualise, and understand vast quantities of data. It will cover the basic principles of data science, including the following topics:
• Exploratory data analysis
• Data visualisation
• A/B Testing and basic statistics
• Regression
• Classification
• Clustering
• Data dimensionality
• Association rule mining
• Time series analysis
• Text mining
This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.
Information on contact teaching time is available from the course guide.
| 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. |
| 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. |
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 | Apply | Students will understand, and be able to use, basic data mining and visualization concepts, techniques and software tools. |
| 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 | Evaluate | Students will be able to design appropriate visualization solutions for different applications, scenarios, and audiences. |
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