Please refer to course overview.
Introduction to IBM SPSS Modeler • Introduction to data science • Describe the CRISP-DM methodology • Introduction to IBM SPSS Modeler • Build models and apply them to new data Collect initial data • Describe field storage • Describe field measurement level • Import from various data formats • Export to various data formats Understand the data • Audit the data • Check for invalid values • Take action for invalid values • Define blanks Set the unit of analysis • Remove duplicates • Aggregate data • Transform nominal fields into flags • Restructure data Integrate data • Append datasets • Merge datasets • Sample records Transform fields • Use the Control Language for Expression Manipulation • Derive fields • Reclassify fields • Bin fields Further field transformations • Use functions • Replace field values • Transform distributions Examine relationships • Examine the relationship between two categorical fields • Examine the relationship between a categorical and continuous field • Examine the relationship between two continuous fields Introduction to modeling • Describe modeling objectives • Create supervised models • Create segmentation models Improve efficiency • Use database scalability by SQL pushback • Process outliers and missing values with the Data Audit node • Use the Set Globals node • Use parameters • Use looping and conditional execution
This course focuses on reviewing concepts of data science, where participants will learn the stages of a data science project. Topics include using automated tools to prepare data for analysis, build models, evaluate models, and deploy models. To learn about these data science concepts and topics, participants will use IBM SPSS Modeler as a tool.
This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v18)) teaches you how to analyze text data using IBM SPSS Modeler Text Analytics. You will be introduced to the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform churn analysis in telecommunications. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource tempates and Text Analysis packages to share with other projects and other users.
This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.
This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.
Clustering and Association Modeling Using IBM SPSS Modeler (v18.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.