Module Details
| Module Code: |
COMP8046 |
| Title: |
Information Analytics
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Long Title:
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Information Analytics
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| NFQ Level: |
Advanced |
| Valid From: |
Semester 1 - 2016/17 ( September 2016 ) |
| Field of Study: |
4811 - Computer Science
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| Module Description: |
In Information Analytics a learner will use a range of analytical techniques to gain valuable insights from data for a specific application domain. The module will focus on the application of machine learning techniques that facilitate the identification of trends and patterns in data over time.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Describe the concepts, principles, methods and techniques of machine learning and its role in knowledge discovery. |
| LO2 |
Utilise data pre-processing and manipulation techniques on data from a specific application domain. |
| LO3 |
Select and apply appropriate machine learning algorithms to a range of datasets. |
| LO4 |
Analyse and interpret patterns and knowledge discovered from the application of machine learning algorithms to problems from a specific application domain. |
| LO5 |
Evaluate the accuracy of machine learning algorithms. |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
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| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
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| No requirements listed |
| Indicative Content |
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Introduction
Overview of terminology and applications in the area data science and analytics.
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Data Extraction and Handling
Importation of data in different formats and from various sources. Cleaning/scrubbing data, data modelling and methods such as reshaping, manipulation and data filtering.
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Predictive Modelling and Classification Methods
Decision Trees Induction, Bayesian, Rule-Based and Ensemble Learning.
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Clustering Analysis
Categories of Clustering (e.g., Partitioning Methods, Hierarchical Methods), Identification of data clusters using k-means algorithm, k-centre approximations, density-based clustering.
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Textual Analysis
Textual Mining and Sentiment analysis using industry standard tools such as SAS.
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Validation
Testing and validating the algorithm accuracy using standard techniques e.g. simple split, k-fold cross-validation, bootstrapping.
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Report Generation and Data Visualisation
Visualisation Techniques and Applications. Ability to visualise data in appropriate forms such as Spatial Data, Multivariate Data Trees, Graphs, etc.
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Analytics in Industry
How data analytics is used within a business setting to monitor performance and identify significant trends in data (customer sentiment, product sales, etc.).
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Module Content & Assessment
|
| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
Assessments
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
|
The University reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Lecture delivering theory underpinning learning outcomes |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Computer Based Lab to support learning outcomes |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Independent Study |
Every Week |
3.00 |
3 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Lecture delivering theory underpinning learning outcomes |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Computer Based Lab to support learning outcomes |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Independent Study |
Every Week |
3.00 |
3 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
|
Module Resources
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| Recommended Book Resources |
|---|
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Ramesh Sharda, Dursun Delen, Efraim Turban. (2014), Business Intelligence and Analytics: Systems for Decision Support, 10th. Pearson, [ISBN: 0133050904].
| | Supplementary Book Resources |
|---|
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Peter Flach.. (2012), Machine learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge, UK; Cambridge University Press, [ISBN: 1107422221].
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Christoper M. Bishop. (2013), Pattern Recognition and Machine Learning, Springer, [ISBN: 8132209060].
| | This module does not have any article/paper resources |
|---|
| Other Resources |
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Website, Andrew Ng. Machine Learning, Coursera,
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Website, Sebestian Thrun. Intro to Machine Learning, Udacity,
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