This KM423G: IBM InfoSphere DataStage v11.x – Advanced Data Processing course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course.
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This official web-based IBM DB2 Analytics Accelerator course provides training in the basics of the IBM DB2 Analytics Accelerator. The course introduces the student to the product layout and functionality and describes the various ways of storing DB2 data in IBM DB2 Analytics Accelerator. In addition to IBM DB2 Analytics Accelerator, it will also teach the student how to set up and work with the IBM Data Studio Accelerator Suite. The demonstrations will explore how tables can be loaded into IBM DB2 Analytics Accelerator, how to predict if tables could run on the accelerator using special register settings, why queries may not be candidates for the accelerator, how to disable or remove tables from the Accelerator, and finally, demonstrate the monitoring feature that is built-into the accelerator studio view.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.
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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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This AMA-2605: Extraordinary Productivity: The 5 Choices That Drive Success productivity seminar combines current neuroscience research with proven productivity principles to help you better manage your decisions, attention, and energy.
You’ll learn how to apply a process—which incorporates the use of Microsoft Outlook®—to dramatically increase your ability to achieve life’s most important outcomes by consistently making choices that create extraordinary value for yourself and your organization. This solution not only produces a measurable increase in productivity but can also provide you with a renewed sense of engagement and accomplishment.
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This TV384G: IBM Agent Builder 6.3.4 course teaches you how to use the IBM Agent Builder software to create, modify, debug, and package agents for use with IBM Performance Management and IBM Tivoli Monitoring products. You create agents that monitor many different data sources. You learn to mix, match, and manipulate monitored data to create custom data points to monitor. You also learn to add custom components specific to the different IBM monitoring environments. IBM Agent Builder can create custom agents for both the IBM Tivoli Monitoring 6.x and IBM Performance Management 8.x on-premises and SaaS environments.
This course is applicable to students from either environment, and a single class might have students from each environment.
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This TX318G: IBM Workload Scheduler 9.4 Administration course teaches the administrative functions necessary to plan, install, configure, maintain, and troubleshoot an IBM Workload Scheduler 9.4 network. This course includes an in-depth look at designing an IBM Workload Scheduler network architecture, background processes in Workload Scheduler, workstation and network configuration options, workload production cycle and processing, effective use of Workload Scheduler security, and techniques for Identifying and resolving common problems.
Hands-on lab exercises include planning a network, defining workstations and domains, and installing and configuring Workload Scheduler components.
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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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Teaches the essential skills required to design, implement, and manage a Red Hat OpenShift Data Foundation cluster and perform day-to-day Kubernetes storage management tasks.
Traditional storage options available to Kubernetes administrators are limited and lack flexibility and/or versatility. Red Hat OpenShift Data Foundation provides real advantages, even when it is backed by cloud storage such as AWS EBS and sophisticated on-prem legacy storage like SAN arrays. Many companies rely on third-party solutions to manage backup and disaster recovery in production. However, proper planning to implement these solutions requires knowledge of the Kubernetes CSI and OAPD APIs. This Red Hat OpenShift course walks the student through the recommended steps of configuring and managing storage services for container and Kubernetes services.
