MSiA 421 Data Mining

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This MSiA 421 Data Mining course module is the part of Statistical Learning Models course, including the Statistical Techniques for Data Mining and Predictive Analysis. This course will define data mining and discuss its relationship to the probabilistic/statistical models. Both methods consist of two types of models: the supervised learning model, the purpose of which is to detect and model the structure in the joint density of several observation variables. This course aims to understand and use unsupervised learning techniques and apply these techniques to large, real-world datasets of businesses. In addition, the system typically uses project techniques that include segmenting customers, personalizing touchpoints, and quantifying the long-term impact of those actions.

Topics include:

• Clustering (k-means, split, hybrid).
• Dimensionality reduction (pivot/factor analysis).
• Recommendation system (association rules, content, collaborative filtering, matrix decomposition method).
• Customer lifecycle value.

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