This course will teach the learner how to use R to develop segmentation and prediction models using key data mining and statistical techniques.

Key Topics Covered:

CRISP-DM Process:

CRISP-DM is a commonly used data mining process, which covers all topics of a data mining project including (i) Business Understanding (ii) Data Understanding, (iii) Data Preparation, (iv) Statistical Analysis, (v) Evaluation and (vi) Deployment.

Segmentation Modelling:

Application of key segmentation techniques including k-means and hierarchical clustering. Learners will be taught all aspects of segmentation modelling including how to (i) prepare the data, (ii) apply the techniques, (iii) assess quality of segmentation models, (iv) profile segments and (v) monitor accuracy of segmentation models.

Prediction Modelling:

Application of key data mining and statistical techniques including classification trees, regression and neural networks. Learners will be taught all aspects of prediction modelling including how to (i) prepare the data, (ii) apply the techniques, (iii) assess the quality of prediction models and (iv) monitor the accuracy of prediction models.

 

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