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Advanced Statistics and Data Mining for Data Science
Data Mining and Statistics
The Course Overview (3:01)
Comparing and Contrasting Statistics and Data Mining (11:19)
Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler (11:38)
Types of Projects (3:58)
Predictive Modeling
Predictive Modeling: Purpose, Examples, and Types (4:37)
Characteristics and Examples of Statistical Predictive Models (2:12)
Linear Regression: Purpose, Formulas, and Demonstration (10:00)
Linear Regression: Assumptions (5:53)
Characteristics and Examples of Decision Trees Models (2:27)
CHAID: Purpose and Theory (2:49)
CHAID Demonstration (5:45)
CHAID Interpretation (9:38)
Characteristics and Examples of Machine Learning Models (2:23)
Neural Network: Purpose and Theory (4:29)
Neural Network Demonstration (7:29)
Comparing Models (5:45)
Cluster Analysis
Cluster Analysis: Purpose Goals, and Applications (5:48)
Cluster Analysis: Basics (12:10)
Cluster Analysis: Models (4:12)
K-Means Demonstration (8:35)
K-Means Interpretation (7:52)
Using Additional Fields to Create a Cluster Profile (6:18)
Association Modeling
Association Modeling Theory: Examples and Objectives (7:24)
Association Modeling Theory: Basics and Applications (7:58)
Demonstration: Apriori Setup and Options (7:59)
Demonstration: Apriori Rule Interpretation (4:50)
Demonstration: Apriori with Tabular Data (6:50)
Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler
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