| Module Code: |
H9DMV |
|
Long Title
|
Data Mining & Visualisation
|
|
Title
|
Data Mining & Visualisation
|
| Module Level: |
LEVEL 9 |
| EQF Level: |
7 |
| EHEA Level: |
Second Cycle |
| Module Coordinator: |
Simon Caton |
| Module Author: |
CRISTINA HAVA MUNTEAN |
| Departments: |
School of Computing
|
| Specifications of the qualifications and experience required of staff |
|
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
LO 1. Apply transformations and statistical operations on datasets to ratify issues of data quality and dimensionality. |
| LO2 |
LO 2. Critically evaluate a variety of data mining techniques with respect to their practical applicability to mobile data. |
| LO3 |
LO 3. Design, develop, and implement processes for data visualization to effectively communicate information. |
| LO4 |
LO 4. Critically review current data mining and visualisation research and research methods in the context of mobile data. |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
|
| No recommendations listed |
Co-requisite Modules
|
| No Co-requisite modules listed |
Module Content & Assessment
| Indicative Content |
|
Introduction (10%)
• Data Mining Methodologies (e.g. KDD, CRISP-DM, and SEMMA)
• Data Mining and Machine Learning
• Exploration and visualisation of sample datasets
• Fundamentals of Visualisation (e.g. Weber's Law, Steven's Power Law, Gestalt Principles, Tufte's Principles of Information Design)
|
|
Data Transformation and Cleaning (30%)
• Attribute selection and discretization
• Projections (e.g., Principal component analysis, random projections, partial least-squares, time series)
• Sampling
• Handling dirty data
• Dimensionality Reductions (e.g. Principle Component Analysis and Linear Discriminant Analysis)
|
|
Data Mining Methods (30%)
• Classification Schemes (e.g., Decision Trees, Bayesian, Regression)
• Link Association and Instance-based Learning (e.g. Association Rules, kNN, Recommender Systems, Network Analysis)
• Clustering Methods (e.g., Partitioning Methods, Hierarchical Methods)
|
|
Visualisation Techniques and Applications (30%)
• Planning and Designing Visualizations
• Technological toolsets for data visualization (e.g., Python, R, D3.js) to include for example: Spatial Data, Geospatial Data, Multivariate Data, Trees, Graphs, Clusters, Rules, and Networks
|
| Assessment Breakdown | % |
|---|
| Coursework | 50.00% |
| End of Module Assessment | 50.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Project |
% of total: |
50 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3 |
| Non-Marked: |
No |
Assessment Description:
Learners must prepare a literary review and analysis covering specific data mining and/or visualisation topics in the domain of mobile technologies. |
|
| End of Module Assessment |
| Assessment Type: |
Terminal Exam |
% of total: |
50 |
| Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
No |
Assessment Description:
n/a |
|
| Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
|
NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
| Module Target Workload Hours 0 Hours |
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
No Description |
1 |
Every Week |
1.00 |
| Tutorial |
No Description |
1 |
Every Week |
1.00 |
| Independent Learning Time |
No Description |
8.5 |
Every Week |
8.50 |
| Total Weekly Contact Hours |
2.00 |
| Workload: Part Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
No Description |
1 |
Every Week |
1.00 |
| Tutorial |
No Description |
1 |
Every Week |
1.00 |
| Independent Learning Time |
No Description |
8.5 |
Every Week |
8.50 |
| Total Weekly Contact Hours |
2.00 |
Module Resources
| Recommended Book Resources |
|---|
-
Brett Lantz. (2013), Machine Learning with R. Packt Publishing..
-
Jared Lander. (2013), R for Everyone: Advanced Analytics and Graphics. Addison-Wesley Professional.
| | Supplementary Book Resources |
|---|
-
Matthew Ward, Georges Grinstein, Daniel Keim. (2010), Interactive Data Visualization: Foundations, Techniques, and Applications, A K Peters Ltd.
-
Colin Ware. (2012), Information Visualization, Third Edition: Perception for Design, 3rd Edition Ed. Morgan Kaufmann.
-
Barker T. (2013), Pro Data Visualization using R and JavaScript, Apress, Apress.
-
Ian H. Witten, Eibe Frank, Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques, Third Edition. Morgan Kaufmann.
-
Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,, Springer.
| | This module does not have any article/paper resources |
|---|
| This module does not have any other resources |
|---|
|
