Data Analysis for Marketing Intelligence
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Goals:
segment the customer base using the main statistical techniques.
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Techniques presented:
multiple regression, logistic regression, multivariate statistical analysis for classification (discriminant analysis, decision trees) and for perceptual mapping and preference analysis (factor analysis, conjoint analysis, multidimensional scaling)._cc781905 -5cde-3194-bb3b-136bad5cf58d_
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Tutorials:
There are exercises for each of the topics covered.
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Prerequisites:
attendance of the INTRO course or having an intermediate knowledge of Statistics for Data Analysis is a prerequisite.
Subjects:
Predictive and interpretative models
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Forecasting a quantitative phenomenon
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Simple linear regression
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The assumptions of the regression model
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Multiple linear regression
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Analyze diagnostics
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Introduction to the generalized linear model
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Introduction to nonlinear models
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Prediction of a qualitative phenomenon
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Logistic regression
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Introduction to loglinear models
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Segmentation models
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The interdependence between the variables
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The distances
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The cluster analysis
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Hierarchical, k-means and two-step methods
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Neural networks (outline)
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Self Organizing Map (Kohonen)
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Classification models
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The discriminant analysis
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Decision trees
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Chaid method and C&RT method
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Stopping rules
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Risk estimation
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Model validation
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Profits, misclassification costs, prior probabilities.
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