IBM

This web based 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. 

The IBM Optim Data Privacy Option course is designed to enable you to successfully develop and execute a data privacy strategy to de-identify sensitive data. This course provides a conceptual understanding of the data privacy issue, as well as the Optim Solution's capabilities for de-identifying data and supporting compliance initiatives for critical business applications. This course serves as a foundation for the Optim Solution's features, and functionality. This course focuses on the technical aspects of the Optim Solution and is intended for technical audiences. You will learn the basics of how the technology works, and how the technology applies to business scenarios. This course does not include information on how to install and configure the technology. This course prepares technical users with the Optim knowledge and skills necessary to participate in an Optim privacy implementation project and confidently perform Optim tasks ongoing. 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.

The IBM Optim Data Growth for z/OS course is designed to enable the students to successfully develop and execute an archiving strategy. This course provides a conceptual understanding of the data growth issue, as well as the Optim Solution's capabilities for managing data growth and improving the performance of critical business applications. This course serves as a foundation for the Optim Solution's features and functionality. This course focuses on the technical aspects of the Optim Solution and is intended for technical audiences. Participants will learn the basics of how the technology works, and how the technology applies to business scenarios. This course does not include information on how to install and configure the technology. This course prepares technical users with the Optim knowledge and skills necessary to participate in an Optim archive implementation project and confidently perform Optim tasks ongoing. Training Paths that reference this course are: InfoSphere Optim Optim Custom Solutions for z/OS – Administrator InfoSphere Optim 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. http://www.ibm.com/training/terms

This course teaches data engineers how to build a robust, fault-tolerant data pipeline that cleans, transforms, and aggregates unorganized and messy data into databases or data sources for IBM Integrated Analytics System. This course is designed to give the participant an overview of the IBM Integrated Analytics System architecture and provide a working knowledge and understanding of the SQL and data engineering best practices.

Students will describe how the Data Masking Pack works; understand how to apply policies for different data types, understand how hash lookup policies work; create a data masking job. 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 teaches you to perform, basic database administrative tasks using DB2 10.1 for Linux, UNIX, and Windows. These tasks include creating and populating databases and implementing a logical design to support recovery requirements. The access strategies selected by the DB2 Optimizer will be examined using the DB2 Explain tools. Various diagnostic methods will be presented, including using the db2diag.log file messages to direct your investigation of problems, as well as using the db2pd commands. 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 teach you how to: Fully use the advanced technical functions and features of DB2 LUW 10.1 and 10.5. Implement DB2 BLU Acceleration, column-organized table support, for a new or existing DB2 database. Describe how the column dictionaries used for DB2 BLU Acceleration are built and utilized to provide extreme compression for column-organized tables. Explain the default workload management used for DB2 BLU Acceleration processing and how you can tailor the WLM objects to efficiently use system resources. Monitor a DB2 database or application that uses column-organized tables using SQL monitor functions. Create optimization profiles that allow applications to control specific operations included in the access plans selected by the DB2 Optimizer, like which index is used to access a table or which join method to utilize for joining tables. Perform advanced monitoring using the DB2 administrative views and routines in SQL queries. Configure and manage the implementation of DB2 instance or database level auditing, including using the db2audit command and creation of audit policies which can be assigned to specific tables, users or database roles to perform selective collection of audit records. Explore DB2's management of disk space usage in Database Managed Storage (DMS) table spaces, including the activities of the rebalancer. Use SQL queries and utilities to check the high water mark on table spaces and to monitor the rebalance operation. Move data from one table to another or from one database to another using utilities like db2move. Utilize the ADMIN_MOVE_TABLE procedure to implement table changes with a minimal impact to data availability for applications. Implement automatic storage management for table spaces and storage groups or enable automatic resize options for DMS managed table spaces to reduce administration requirements and complexity. Exploit and monitor the REORG utility processing for offline and online table, and index reorganization This includes planning for the disk space and database log space necessary for reorganization. Utilize the REORG Utility to implement row compression for large tables, to reduce disk utilization and improve I/O performance for a DB2 database and understand the automatic creation of compression dictionaries. For Multidimensional Clustering (MDC) tables, determine how to select the dimension columns and table space extent size for efficient implementation of MDC tables. Compare the block indexes used with MDC tables with rows based indexes. Select the MDC rollout option that best matches application needs and achieves the best performance results. Plan and implement range based table partitioning for large DB2 tables. Utilize the ALTER TABLE ATTACH and DETACH options to support roll-in and roll-out operations for range-partitioned tables. Compare the advantages of selecting or combining range partitioning with the hash-based partitioning used in DB2 partitioned databases or the multiple dimensions provided by MDC tables. You get practical experience in the planning and utilization of a wide variety DB2 LUW utilities and functions by performing a series of lab exercises using DB2 Advanced Enterprise Edition 10.5 installed on a Linux platform. The exercises build skills that can be applied to DB2 database servers on any Linux, UNIX or Windows environment.   The course materials cover DB2 10.5 for Linux, UNIX, and Windows. 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 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. 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 brings the benefit of predictive analytics to real business problems, allowing you to build custom applications tailored to your customers or industry. While applications are typically configured to solve very specific problems, all are based on a common set of capabilities: Automate decisions using business rules; add insight using predictive models; and use prioritization, optimization, or simulation to reach the best decision based on the above. A number of packaged applications are available, tailored to solving specific business problems. The course will not only cover how to use the packaged applications, but also how to create your own applications, how Analytical Decision Management interplays with IBM SPSS Modeler (v18) and IBM SPSS Collaboration and Deployment Services (v8), and how to deploy results for real-time.

This course is an intermediate course designed to teach Collaboration and Deployment users object and asset management, security, shared resource usage, automation, and interaction with IBM SPSS Modeler Gold. Students focus on the makeup of the content repository and its objects. They will learn how to manage repository objects, the logical hierarchy structure, and how to import, export, and promote objects for use in multi-repository environments. Students will become familiar with the components of jobs and the mechanisms to set up, order, and relate job steps. Scheduling, parameters, job monitoring, job history, and event notification are discussed. Finally, the role of Collaboration and Deployment Services in Modeler Gold is discussed, addressing Real Time Scoring, Analytic Data View, and Model Management.

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.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace. This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis process. Students will learn the basics of reading data, data definition, data modification, and data analysis and presentation of analytical results. Students will also see how easy it is to get data into IBM SPSS Statistics so that they can focus on analyzing the information. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base features. 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. http://www.ibm.com/training/terms

This 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, as well as 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 as well as how to apply them with confidence, interpret their output, and graphically display the results. 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. http://www.ibm.com/training/terms

This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.

This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases. 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. http://www.ibm.com/training/terms

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.

This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.

This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v18)) teaches you how to analyze text data using IBM SPSS Modeler Text Analytics. You will be introduced to the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform churn analysis in telecommunications. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource tempates and Text Analysis packages to share with other projects and other users. 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. http://www.ibm.com/training/terms