This course provides the fundamentals of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.
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0A018G – IBM Data science without a Ph.D. Using IBM SPSS Modeler (v18.1.1)
This course focuses on reviewing concepts of data science, where students 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, students will use IBM SPSS Modeler as a tool.
0A028G – Introduction to Time Series Analysis Using IBM SPSS Modeler (V18.1.1)
This course gets students up and running with a set of procedures for analyzing time series data. Students will learn how to forecast using a variety of models, including regression, exponential smoothing, and ARIMA, which take into account different combinations of trend and seasonality. The Expert Modeler features will be covered, which is designed to automatically select the best fitting exponential smoothing or ARIMA model, but students will also learn how to specify their own custom models, and also how to identify ARIMA models themselves using a variety of diagnostic tools such as time plots and autocorrelation plots.
0A038G – Advanced Predictive Modeling Using IBM SPSS Modeler
This course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.
0A048G – Clustering and Association Modeling Using IBM SPSS Modeler
Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1) 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.
0A058G – Advanced Data Preparation Using IBM SPSS Modeler
This course covers advanced topics to aid in the preparation of data for a successful data science project. Students will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.
0A0U8G – Predictive Modeling for Categorical Targets Using IBM SPSS Modeler
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.
0A0V8G – Predictive Modeling for Continuous Targets Using IBM SPSS Modeler
This course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.
0A108G – Introduction to IBM SPSS Modeler Text Analytics (V18.1.1)
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. Students 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 templates and Text Analysis packages to share with other projects and other users.
0G056G – Correspondence Analysis and Multidimensional Scaling with IBM SPSS Categories
This course will focus on how to perform Correspondence Analysis and Multi-Dimensional Scaling using procedures in the IBM SPSS Categories add-on module in IBM SPSS Statistics. Learn how to use correspondence analysis to examine the relationship of categorical data and display these relationships on perceptual maps. Learn about multidimensional scaling and preference scaling techniques to examine similarities and dissimilarities among objects such as product brands and features and customer preferences. These techniques are useful in any circumstance where you need to analyze and display graphically the correspondence among categorical data. The course will discuss the basic logic of these techniques, how to setup the analysis and examine the results using a variety of usage examples and hands-on exercises.