Short Title:Data Mining
Full Title:Data Mining
Module Code:DMKT H4015
ECTS credits: 5
NFQ Level:8
Module Delivered in 1 programme(s)
Module Contributor:Brian Watters
Module Description:The purpose of this module is to equip the students with the skills and techniques used in the exploration of data. On completion of this module students will be able to support a wide range of business intelligence activities such as customer profiling, targeted marketing, store location, basket analysis and many others.
Learning Outcomes:
On successful completion of this module the learner will be able to
  1. recommend the appropriate data mining tools, techniques and technologies relevant to the digital marketing environment.
  2. extract, load and transform marketing data in preparation for analysis
  3. critically analyse and evaluate marketing data in order to formulate recommendations for the development of marketing strategy.
  4. research, explore, analyse and identify emerging trends embodied in marketing data.

Module Content & Assessment

Indicative Content
Data Preprocessing
data cleaning, missing data, graphical methods, measures of centre and spread, data transformation, min-max normalisation, z-score standardisation, flag variables, trasforming categorical data to numerical data, binning.
Exploratory Data Analysis
Hypothesis testing versus exploratory data analysis, exploring categorical and numerical variables, exploring multivariate relationships, data subsets, binning based on predicative values, deriving new variables, correlated predictor variables.
Audience Segmentation (Cluster Analysis)
Summarization, compression, cluster types, K-means, agglomerative hierarchical clustering, DBSCAN, cluster evaluation, density based clustering, graph based clustering.
Data Mining for Customer Acquisition and Retention (Classification)
Decision tree induction, model overfitting, model performance evaluation, comparing models, rule based classifiers, nearest neighbour classifiers, bayesian classifiers, support vector machines.
Data Mining for Cross-Selling and Bundled Marketing (Association)
affinity analysis and market basket analysis, support, confidence, frequent itemset generation, rule generation, compact representation of frequent itemsets, A Priori Algorithm, association rule evaluation, supervised v unsupervised learning.
Text Analytics, Text Mining, and Sentiment Analysis
Natural Language processing, text mining applications, text mining processes, classification, clustering, association, trend analysis, text mining tools, sentiment analysis, sentiment analysis applications, sentiment analysis process, sentiment and speech analytics.
Indicative Assessment Breakdown%
Course Work Assessment %100.00%
Course Work Assessment %
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Written Report A Literature Review on the use and impact of data mining tools and techniques in the marketing environment. 4 30.00 Week 3
Performance Evaluation Data Extraction Project. A practical project of the techniques required for the extraction, transformation and loading of data in preparation for mining. 2 15.00 Week 5
Written Report A written report on the range, purpose and efficacy of data mining tools currently available to marketeers. 1 15.00 Week 7
Portfolio Students are required to use a data mining tool to carry out exploratory analysis of a data set. Their findings will be presented using a portfolio of materials - practical project (accounting for 30% overall), written report (accounting for 5% overall) and a presentation (accounting for 5% overall). 3 40.00 Sem 1 End
No Final Exam Assessment %
Indicative Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
Students will be required to resubmit practical materials as specified by the examiner. These materials will be designed to address weaknesses identified during the semester.

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


Indicative Module Workload & Resources

Indicative Workload: Full Time
Frequency Indicative Average Weekly Learner Workload
Every Week 2.00
Every Week 2.00
Every Week 4.00
Recommended Book Resources
  • Daniel T. Larose, Chantal D. Larose 2014, Discovering Knowledge in Data: An Introduction to Data Mining, 2nd Ed., Wiley Hoboken, NJ
Supplementary Book Resources
  • Pang-Ning Tan, Michael Steinbach, Vipin Kumar 2014, Introduction to Data Mining, 1st Ed., Pearson Harrow UK
  • Susan Chiu, Domingo Tavella 2008, Data mining and market intelligence for optimal marketing returns, 1st Ed., Butterworth-Heinemann/Elsevier Oxford, UK [ISBN: 9780750682343]
  • Andrea Ahlemeyer-Stubbe, Shirley Coleman 2014, A practical guide to data mining for business and industry, 1st Ed., Wiley Chichester UK
  • Edited by Yukio Ohsawa Katsutoshi Yada 2009, Data Mining for Design and Marketing, CRC Press Boca Raton,USA
  • Daniel T. Larose, Chantal D. Larose 2015, Data Mining and Predictive Analytics, 2nd Ed., Wiley Hoboken, New Jersey
  • Sang C. Suh 2012, Practical Applications of Data Mining, 1st Ed., Jones & Bartlett Learning Sudbury, MA [ISBN: 9780763785871]
  • Ramesh Sharda, Dursun Delen, Efraim Turban ; with contributions by J. E. Aronson, Ting-Peng Liang, David King. 2014, Business intelligence and analytics, 10th Global Edition Ed., Pearson Boston [ISBN: 9781292009209]
This module does not have any article/paper resources
Other Resources

Module Delivered in

Programme Code Programme Semester Delivery
BN_BDMKT_8 Bachelor of Arts (Honours) in Digital Marketing 8 Mandatory