This 0A008G: Introduction to IBM SPSS Modeler and Data Science v18.x 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 provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.1.1, and introduces the student to modeling.
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This IBM Predictive Modeling training 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 CR 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.
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This 0A079G: Introduction to Machine Learning Models Using IBM SPSS Modeler v18.2 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.
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This 0A069G: IBM SPSS Modeler Foundations v18.2 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.
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In this W7069G: IBM Watson OpenScale Methodology training course, uou will learn how Watson OpenScale lets business analysts, data scientists, and developers build monitors for artificial intelligence (AI) models to manage risks. You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will also learn how monitoring for unwanted biases and viewing explanations of predictions helps provide business stakeholders confidence in the AI being launched into production.
Note: This course contains the same topics as 6X240G IBM Watson OpenScale on IBM Cloud Pak for Data WBT.
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This ZZ930G: InfoSphere MDM Physical Domains course is designed for anyone who wants to get an understanding of the Data Domains for the InfoSphere Master Data Management Physical Module. This course takes a comprehensive look at the three core data domains of InfoSphere MDM: Party, Account, and Product. For each of the domains spanned by InfoSphere MDM, participants will be exposed to the data model, services, and rules associated with the main entities of that domain. Heavy emphasis is put on exercises and activities so that the participants can apply the knowledge that they learn after course conclusion.
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This 0A039G: Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2) course presents advanced models available in IBM SPSS Modeler. The participant 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.
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The course is updated for DB2 10 for z/OS. This course is the classroom delivered version of the Instructor led Online course
- DB2 10 for z/OS System Administration – ILO (3V851).
Administrators of DB2 10 for z/OS can acquire a view of the architecture and fundamental processes required to manage a DB2 10 for z/OS subsystem. Engage in lectures and hands-on labs to gain experience to:
- Relate the z/OS IPL process to a DB2 subsystem
- Explain effects of stopping and starting DB2
- Explain how DB2 sets and use Integrated Catalog Facility (ICF) catalog names
- The use of DSN command processor running in batch and foreground
- Use views to minimize your ability to see into the DB2 catalog
- See how the catalog (through grant activity) controls access to data
- Search the catalog for problem situations
- Use the catalog and DB2 utilities to determine data recovery requirements
- Describe Internal Resource Lock Manager (IRLM) in a DB2 environment
- Implement DB2 and Resource Access Control Facility (RACF) security
- Describe DB2 program flow for all environments
- Display normal and problem threads and database status
- See how the SQL Processor Using File Input (SPUFI) AUTOCOMMIT option defers the COMMIT/ROLLBACK decision
- Interpret lock displays
- Identify and cancel particular threads
- Describe available DB2 utilities to manage system and user page sets
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This course enables the project administrators and ETL developers to acquire the skills necessary to develop parallel jobs in DataStage. The emphasis is on developers. Only administrative functions that are relevant to DataStage developers are fully discussed. Students will learn to create parallel jobs that access sequential and relational data and combine and transform the data using functions and other job components.
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This B6158G: IBM Cognos Analytics: Author Reports Fundamentals v11.0.x offering provides Business and Professional Authors with an introduction to report building techniques using relational data models. Techniques to enhance, customize, and manage professional reports will be explored. Activities will illustrate and reinforce key concepts during this learning opportunity.
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This course teaches Information Server and/or DataStage administrators to configure, manage, and monitor the DataStage Engine which plays a crucial role in Information Server. It not only runs high performance parallel ETL jobs designed and built in DataStage. It also supports other Information Server products including Information Analyzer, QualityStage, and Data Click. After introducing DataStage parallel jobs and the Engine that runs them, the course describes DataStage project configuration, the Engine’s development and runtime environments, and the Engine’s data source connectivity. In addition the course explains how to import and export DataStage objects, how to run and monitor DataStage jobs through the command line and GUI, and how to use some important Engine utilities.
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This course enables students to acquire the skills necessary to use the Information Governance Catalog to analyze metadata stored within the Information Server Repository. The emphasis is on how metadata gets captured within the repository and how to explore and analyze the metadata it contains.
