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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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.
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.
0G51BG – Statistical Analysis Using IBM SPSS Statistics (V26)
This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics.
In this course, students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques and how to apply them with confidence, interpret their output, and graphically display the results.
Students Will Learn:
– Introduction to statistical analysis
– Describing individual variables
– Testing hypotheses
– Testing hypotheses on individual variables
– Testing on the relationship between categorical variables
– Testing on the difference between two group means
– Testing on differences between more than two group means
– Testing on the relationship between scale variables
– Predicting a scale variable: Regression
– Introduction to Bayesian statistics
– Overview of multivariate procedures
0G53BG – IBM SPSS Statistics Essentials (V26)
This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis. Students will learn the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base; one section presents an add-on module, IBM SPSS Custom Tables.
0K2K9G – Introduction to IBM SPSS Decision Trees (v19) SPVC
Introduction to IBM SPSS Decision Trees is a two day self-paced training course that covers the principles and practice of the tree-based decision and regression methods available in IBM SPSS Decision Trees. A general introduction to the features of the IBM SPSS Decision Trees module and an overview of decision tree based methods will be covered. These methods (CHAID, Exhaustive CHAID, CRT, and QUEST) are used to perform classification, segmentation, and prediction modeling in a wide range of business and research areas. The techniques are discussed and compared, analyses are performed, and the results interpreted.
1M424G – IBM InfoSphere DataStage v11.5 Data Masking – SPVC
In this course students will describe how the Data Masking Pack works. They will understand how to apply policies for different data types, how hash lookup policies work, and create a data masking job.
1Z801G – Introduction to InfoSphere Master Data Management v11.3
This course is designed to teach students the basics of Master Data Management and IBM InfoSphere solutions. Students will gain an understanding of what Master Data Management is and IBM’s InfoSphere MDM offerings: Standard, Advanced, and Collaborative Editions.
1Z802G – IBM InfoSphere Master Data Management Fundamentals (V11.6) – SPVC
This course will build a foundation for students interested in what master data is and how it is managed. Students will learn about master data management (MDM), MDM implementation styles, and a variety of MDM use cases. They will then be introduced to multiple IBM MDM solutions and will gain an understanding of the capabilities of each solution.