Long Title:Data and Web Mining
Language of Instruction:English
Module Code:H8DWM
Credits: 10
NFQ Level:LEVEL 8
Field of Study: Software and applications development and analysis
Module Delivered in 3 programme(s)
Module Coordinator: Simon Caton
Module editor: Simon Caton
Teaching and Learning Strategy: The teaching and learning strategy involves the use of lectures to establish the theoretical foundations of data and web mining. In class guided practical sessions augment the theoretical aspects of the course, and are reinforced with practical sessions in teaching laboratories.
Learning Environment: Learning will take place in a lab environment, each student will have access to a PC with Weka/RapidMiner Data mining tools. Learners will have access to library resources and to faculty outside of the classroom where required. Module materials will be placed on Moodle, the college’s LMS. Labs will concentrate on use of data mining tools, (e.g. Weka/RapidMiner), and how best to implement the theory learned during the module.
Module Description: The module aims to introduce the learners to a set of data mining techniques including classification trees, neural networks, and support vector machines. • The module will address a number of issues relating to understanding and optimising the performance of data mining algorithms and review methods to evaluate models.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Apply transformations and statistical operations to datasets and assess the factors that impact on data quality
LO2 Apply a variety of data mining techniques and identify their practical applicability to various problem domains
LO3 Independently research current trends and developments in knowledge discovery related technologies and use this skill to critically analyse publications to assess the relative merits of various technologies
LO4 Contextualise how web search engines crawl, index, and rank web content, and assess how the web is structured
LO5 Apply fundamental web data mining concepts and techniques
Pre-requisite learning
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
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.
No requirements listed
 

Module Content & Assessment

Indicative Content
1. Data Analysis and Mining Overview (15%)
Data vs. information Data mining and machine learning Structural descriptions and rules for classification and association Exploration of sample datasets Fielded applications (e.g., ranking web pages, loan applications, screening images, load forecasting, machine fault diagnosis, market basket analysis) Generalization as search Data mining and ethics
2. Data Transformations (15%)
Attribute selection and discretization Projections (e.g., Principal component analysis, random projections, partial least-squares, text, time series) Sampling Handling dirty data
3. Knowledge Representation and Machine Learning Schemes (50%)
Tables Linear models Trees Rules based systems for knowledge representation Instance-based representation Inferring rudimentary rules Statistical modelling Historical evolution and foundations of AI Approaches to machine learning (e.g., decision tree learning, association rule learning, clustering) Utilising machine learning application software environments (e.g., Weka, R, RapidMiner etc.) for data mining and data visualisation
4. Extracting Data from the Web (20%)
Web crawler operations Search engines implementation Identification of search trends Search Engine Optimisation (SEO) Web usage, web content, and web structure mining Social media data mining
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Full Time

Coursework
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Continuous Assessment (0200) Literature Review 1,2,3 20.00 n/a
Project (0050) Group Project 1,2,3 30.00 n/a
End of Module Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Terminal Exam End-of-Semester Final Examination 1,2,3,4,5 50.00 End-of-Semester
No Workplace Assessment
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.
Reassessment Description
Learners will be afforded an opportunity to repeat the final examination and all learning outcomes will be assessed in the repeat sitting.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2 Every Week 2.00
Tutorial No Description 2 Every Week 2.00
Independent Learning Time No Description 6.5 Every Week 6.50
Total Hours 10.50
Total Weekly Learner Workload 10.50
Total Weekly Contact Hours 4.00
This module has no Part Time workload.
 

Module Resources

Recommended Book Resources
  • Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer [ISBN: 3642194591.]
  • Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann [ISBN: 0123748569.]
  • Matthew A. Russell, Mining the Social Web, O'Reilly Media [ISBN: 1449388345]
  • Brett Lantz. 2015, Machine learning with R, 2 Ed., Packt Pub Birmingham, UK [ISBN: 9781784393908]
Supplementary Book Resources
  • Michael R. Berthold (Editor), David J. Hand (Editor), Intelligent Data Analysis, Springer [ISBN: 3642077072.]
  • Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, Third Edition, Morgan Kaufmann [ISBN: 0123814790]
  • Rajaraman A., Ullman J., 2011, Mining of Massive Datasets, Free on-line edition available at: http://infolab.stanford.edu/~ullman/mmds.html Ed., Cambridge Press
  • Kevin Warwick, Artificial Intelligence: The Basics, Routledge [ISBN: 0415564832]
  • Stuart J. Russell and Peter Norvig; contributing writers, Ernest Davis... [et al.] 2010, Artificial intelligence, Prentice Hall Upper Saddle River, N.J. [ISBN: 0136042597]
  • Pang-Ning Tan, Michael Steinbach, Vipin Kumar 2006, Introduction to data mining, Pearson Addison Wesley Boston [ISBN: 0321321367.]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
BSHTM B.Sc. (Hons) in Technology Management 8 Group Elective 1
BSHC BSc (Honours) in Computing 8 Optional
HDSDA Higher Diploma in Science in Data Analytics 2 Core Subject