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.
0A039G – Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)
This course presents advanced models available in IBM SPSS Modeler. The student is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.
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.
0A069G – IBM SPSS Modeler Foundations (V18.2)
This course provides the foundations 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 provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and introduces the student to modeling.
0A079G – Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)
This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.
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.