Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Web Mining | |||
| Reference | CMM724 | Version | 3 |
| Created | February 2024 | SCQF Level | SCQF 11 |
| Approved | April 2017 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To introduce the student to process qualitative and quantitative web data using advanced analytical techniques to make business decisions. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Analyse the main concepts of current and intelligent technologies for information search and retrieval applications. |
| 2 | Criticise the effectiveness of state-of-the-art extraction and retrieval tool kits for web mining and prevalent technologies for web analytics. |
| 3 | Produce a solution to derive web analytics for a given online business application using an intelligent information extraction toolkit. |
| 4 | Design a comparative study to evaluate and interpret results from designing and developing information retrieval and extraction systems. |
| Indicative Module Content |
|---|
| Information collection: crawling and document pre-processing. Information retrieval: document Indexing, similarity metrics and clustering. Web Analytics: Google analytics and web information analysis. Text classification: feature-vector representations, information extraction, classification with support vector machines. Case studies: topic modelling applied to news articles, intelligent search and browse, sentiment analysis and social media mining. |
| Module Delivery |
|---|
| This is a lecture based course, supplemented with laboratory sessions, where state-of-the-art extraction and retrieval toolkits will be applied to varied case studies. Tutorials will be used to initiate discussions on research papers from the field to supplement the lectures. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | N/A | 48 |
| Non-Contact Hours | N/A | 102 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | N/A | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Coursework submission worth 100% of total module assessment. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in Component 1. |
| B | The student needs to achieve a B in Component 1. |
| C | The student needs to achieve a C in Component 1. |
| D | The student needs to achieve a D in Component 1. |
| E | The student needs to achieve an E in Component 1. |
| F | The student needs to achieve an F in Component 1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | KAUSHIK A., 2009. Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity. Sybex. |
| 2 | CLIFTON B., 2012. Advanced Web Metrics with Google Analytics. Sybex. |
| 3 | BEASLEY M., 2013. Practical Web Analytics for User Experience: How Analytics Can Help You Understand Your Users. Morgan Kaufman. |
| 4 | BURBY J. and ATCHISON S., 2007. Actionable Web Analytics: Using Data to Make Smart Business Decisions. Sybex. |