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.
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.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |