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IBM Courses

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0A008G – Introduction to IBM SPSS Modeler and Data Science v18.1.1

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.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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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.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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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.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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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.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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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.

  • Self-Paced Virtual Classroom
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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.

  • Self-Paced Virtual Classroom
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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.

  • Self-Paced Virtual Classroom
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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.

  • Self-Paced Virtual Classroom
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2E131G – DB2 SQL for Experienced Users

This course teaches students how to make use of advanced SQL techniques to access DB2 databases in different environments. This course is appropriate for individuals working in all DB2 environments, specifically for z/OS, Linux, UNIX, and Windows.

  • Self-Paced Virtual Classroom
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2L285G – Quick Basics of DB2 10.5 Administration for Windows

This course teaches students how to perform, basic database administrative tasks using DB2 10.5 for Linux, UNIX, and Windows. These tasks include creating database objects like table, indexes and views and loading data into the database with DB2 utilities like LOAD and INGEST. Various diagnostic methods will be presented, including using various db2pd command options and monitoring using SQL statements with DB2 monitor functions. Students will learn how to implement automatic archival for database logs and how to recover a database to a specific point in time using the archived logs. The locking performed by DB2 and the effect the application isolation level has on locking and lock wait conditions are covered. This course provides a quick start to DB2 database administration skills for experienced relational Database Administrators (DBA).

The lab exercises are performed using DB2 LUW 10.5 for Windows. For some exercise tasks students will have the option to complete the task using a DB2 command line processor or using the graphical interface provided by IBM Data Server Manager.

  • Self-Paced Virtual Classroom
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