Please refer to course overview
1: Introduction to data science and IBM SPSS Modeler • Explain the stages in a data-science project, using the CRISP-DM methodology • Create IBM SPSS Modeler streams • Build and apply a machine learning model 2: Setting measurement levels • Explain the concept of "field measurement level" • Explain the consequences of incorrect measurement levels • Modify a field's measurement level 3: Exploring the data • Audit the data • Check for invalid values • Take action for invalid values • Impute missing values • Replace outliers and extremes 4: Using automated data preparation • Automatically exclude low quality fields • Automatically replace missing values • Automatically replace outliers and extremes 5: Partitioning the data • Explain the rationale for partitioning the data • Partition the data into a training set and testing set 6: Selecting predictors • Automatically select important predictors (features) to predict a target • Explain the limitations of automatically selecting features 7: Using automated modeling • Find the best model for categorical targets • Find the best model for continuous targets • Explain what an ensemble model is 8: Evaluating models • Evaluate models for categorical targets • Evaluate models for continuous targets 9: Deploying models • List two ways to deploy models • Export scored data
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.