This course provides you with information about the functions of IBM’s DB2, a relational database manager which may be installed under a variety of operating systems on many hardware platforms.
DB2 runs under the z/OS, VM, Linux, UNIX, and Windows operating systems, to name a few.
The course includes discussion of how the DB2 products provide services. The focus is on the services DB2 provides and how we work with DB2, not on its internal workings.
This offering covers the fundamental concepts of installing and configuring IBM Cognos Analytics, and administering servers and content, in a distributed environment. In the course, participants will identify requirements for the installation and configuration of a distributed IBM Cognos Analytics software environment, implement security in the environment, and manage the server components. Students will also monitor and schedule tasks, create data sources, and manage and deploy content in the portal and IBM Cognos Administration.
This offering covers the fundamental concepts of installing and configuring IBM Cognos Analytics, and administering servers and content, in a distributed environment. In the course, participants will identify requirements for the installation and configuration of a distributed IBM Cognos Analytics software environment, implement security in the environment, and manage the server components. Students will also monitor and schedule tasks, create data sources, and manage and deploy content in the portal and IBM Cognos Administration.
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.
This 0K53BG: IBM SPSS Statistics Essentials v26 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.
This 0K51BG: Statistical Analysis Using IBM SPSS Statistics course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. 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.
This course is also available as classroom course Administration of IBM DataPower Gateway V7.6 (WE761G).
IBM DataPower Gateway Appliances are network devices that help secure, integrate, and optimize access to web, web services, mobile, and API workloads. Through instructor-led lectures and hands-on lab exercises, you learn how to run various administrative procedures, from initial installation and setup through ongoing maintenance of the appliances in production. You learn about the available management interfaces, such as the command-line interface (CLI), Web Management graphical interface, and XML Management Interface. You also learn how to use these interfaces to run various administrative tasks, such as upgrading firmware, running backup and restore operations, and configuring user accounts and domains. The course includes some information on upgrading firmware and working with DataPower hardware appliances.
This course exercises uses the following appliances:
DataPower Gateway Virtual Edition
Information in the course units also applies to other DataPower appliances.
The lab environment for this course uses the Ubuntu Linux Operating System on an ESX image that runs on the IBM Remote Lab Platform.
This course is designed to introduce you to the capabilities of BigSheets. BigSheets is a component of IBM BigInsights through the Analyst and the Data Scientist module. It provides the analyst the ability to be able to visualize and analyze data stored on the HDFS using a spreadsheet type interface without any programming.
This course is also available as classroom course WM513G: IBM MQ V9 Application Development (Windows Labs)
This course helps you develop the skills that are necessary to implement various application requirements on IBM MQ versions up to and including IBM MQ V9.0.2. It focuses on procedural application development for IBM MQ.
The course begins by describing IBM MQ and the effect of design and development choices in the IBM MQ environment. It then covers IBM MQ application programming topics such as methods of putting and getting messages, identifying code that creates queue manager affinities, working with transactions, and uses of the publish/subscribe messaging style.
Finally, the course describes the IBM MQ Light interface, introduces Advanced Message Queuing Protocol (AMQP), and explains how to set up an AMQP channel and how to interface with IBM MQ Light.
Hands-on exercises throughout the course reinforce the lecture material and give you experience with IBM MQ clients.
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. /terms
This course will step you through the QualityStage data cleansing process. You will transform an unstructured data source into a format suitable for loading into an existing data target. You will cleanse the source data by building a customer rule set that you create and use that rule set to standardize the data. You will next build a reference match to relate the cleansed source data to the existing target data.
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.
This course is designed to introduce advanced parallel job development techniques in DataStage v11.5. In this course you will develop a deeper understanding of the DataStage architecture, including a deeper understanding of the DataStage development and runtime environments. This will enable you to design parallel jobs that are robust, less subject to errors, reusable, and optimized for better performance.
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.
This F2930G: IBM Case Manager: Solution Deployment from Design to Production v5.3.2 course is designed to show you how to migrate and deploy Case Manager solutions from development to preproduction and production. Participants use a system with two instances of IBM Case Manager to practice the skills that are required by solution builders and administrators to migrate and deploy solutions from one environment to another.
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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