Contains: instructional and interactive content, demonstrations and hand-on simulated exercises.

IBM Cognos Analytics for Consumers (v11.0) will teach consumers how to access content, use reports, create dashboards, and personalize the appearance of IBM Cognos Analytics portal.

This course is designed to teach participants how to identify components and sub-components of the IBM Cognos Analytics architecture and how to use tools and techniques to provide a foundation to troubleshoot issues. Through lecture and interactive exercises participants will identify IBM Cognos Analytics components, examine how these components interact with Java, and will explore logging to assist when troubleshooting issues.

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course is designed to teach participants how to identify components and sub-components of the IBM Cognos Analytics architecture and how to use tools and techniques to provide a foundation to troubleshoot issues. Through lecture and interactive exercises participants will identify IBM Cognos Analytics components, examine how these components interact with Java, and will explore logging to assist when troubleshooting issues.

This offering teaches Professional Report Authors about advanced report building techniques using relational data models, dimensional data, and ways of enhancing, customizing, managing, and distributing professional reports. The course builds on topics presented in the Fundamentals course. Activities will illustrate and reinforce key concepts during this learning activity.

This course teaches experienced authors advanced report building techniques to enhance, customize, manage, and distribute reports. Additionally, the student will learn how to create highly interactive and engaging reports that can be run offline by creating Active Reports.

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course teaches experienced authors advanced report building techniques to enhance, customize, manage, and distribute reports. Additionally, the student will learn how to create highly interactive and engaging reports that can be run offline by creating Active Reports.

This 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.

This course provides authors with an introduction to build reports using Cognos Analytics. Techniques to enhance, customize, and manage reports will be explored. Activities will illustrate and reinforce key concepts during this learning opportunity.

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course provides authors with an introduction to build reports using Cognos Analytics. Techniques to enhance, customize, and manage reports will be explored. Activities will illustrate and reinforce key concepts during this learning opportunity.

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.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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 training teaches data modelers how to model data using data modules in IBM Cognos Analytics. Users will learn how to create data modules from different sources, such as uploaded files. They will also identify how to customize their data modules by adding joins, calculations, and filters. In addition, they will examine how to group their data (for example, by using navigation paths), how to share their data modules with others, and how to make use of some advanced modeling techniques, such as relative date analysis.

This course teaches authors, with basic knowledge of group accounting and Microsoft Excel, how to design and generate financial reports using IBM Cognos Controller. Students will learn how to create ad hoc and standard reports to analyze data. They will also develop custom reports using the Report Generator utility and the Excel Link. In addition, students will learn how to run multiple reports at the same time with report books.

This course provides participants with introductory to advanced knowledge of how to model metadata for predictable reporting and analysis results using IBM Cognos Cube Designer. Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing a dynamic cube, and enabling end users to easily author reports and analyze data.

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course provides participants with introductory to advanced knowledge of how to model metadata for predictable reporting and analysis results using IBM Cognos Cube Designer. Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing a dynamic cube, and enabling end users to easily author reports and analyze data.

This offering provides participants with introductory to advanced knowledge of metadata modeling concepts, and how to model metadata for predictable reporting and analysis results using Framework Manager. Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing of metadata to the web, enabling end users to easily author reports and analyze data.

This offering provides participants with introductory to advanced knowledge of metadata modeling concepts, and how to model metadata for predictable reporting and analysis results using Framework Manager. Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing of metadata to the web, enabling end users to easily author reports and analyze data.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This offering provides participants with introductory to advanced knowledge of metadata modeling concepts, and how to model metadata for predictable reporting and analysis results using IBM Cognos Framework Manager. Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing of metadata to the web, enabling end users to easily author reports and analyze data.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This offering provides participants with introductory to advanced knowledge of metadata modeling concepts, and how to model metadata for predictable reporting and analysis results using IBM Cognos Framework Manager. Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing of metadata to the web, enabling end users to easily author reports and analyze data.

In this course, you will learn how to use the IBM InfoSphere suite to analyze data and report results to business users. Information discovered during analysis will be used to construct data rules. This course will also explore techniques for delivering data analysis results to ETL developers and demonstrate how to develop more meaningful meta data to reflect data discovery results. An information analysis methodology and a case study will be used to guide exercises.

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.

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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.

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.

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.

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This 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.

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.

In this course students learn how the Information Governance Catalog is used to govern information assets through the development of a governance catalog of categories and terms. This catalog documents information assets and governance policies and rules that implement the high-level strategy and objectives of a governance program.

In this course students learn how the Information Governance Catalog is used to govern information assets through the development of a governance catalog of categories and terms. This catalog documents information assets and governance policies and rules that implement the high-level strategy and objectives of a governance program.

Overview

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.

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. 

This course gets those charged with administering Information Server v11.5 and its suite of many products and components started with the basic administrative tasks necessary to support Information Server users and developers.  The course begins with a functional overview of Information Server and the products and components that support these functions.  Then it focuses on the basic administrative tasks an Information Server administration will need to perform including user management, session management, and reporting management tasks.  The course covers both the use of Information Server administrative clients such as the Administration Console and Metadata Asset Manager and the use of command line tools such as istool and encrypt.

This course will build a foundation for students interested in what master data is and how it is managed. The student will learn about master data management (MDM), MDM implementation styles, and a variety of MDM use cases. The student will then be introduced to multiple IBM MDM solutions and will gain an understanding of the capabilities of each solution.

This course provides participants with advanced administration skills for IBM Integrated Analytics System. This course is designed to give participants an overview of the IIAS system, show how to administer the system using the console and command line interface, and extend the appliance. Participants will also monitor the system and performance in addition to managing users and security.

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 datasources 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.

This course teaches data scientists how to use the data science capabilities of IBM Integrated Analytics System, using Watson Studio, RStudio, Spark, and in-database analytics. 

This course provides Administrators with guidance on installing and administering the IBM Planning Analytics - Local environment. The course outlines how the architecture can be customized to fit into various infrastructures. Students will learn how to install and configure IBM Planning Analytics - Local, monitor system performance, and secure applications.

Note: Guided eLearning is a self-paced offering which includes web-based content for self-study and videos (including audio) that demonstrate activities.

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.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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.

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.

IBM Stewardship Center for Information Server v11.5 provides event notification with workflow and remediation. Business users can be notified and respond to Information Server events, when they occur. These events include data quality exceptions occurring in Information Server products such as DataStage and Information Analyzer. These events also include catalog edits to governance categories and terms occurring within the Information Governance Catalog.

The InfoSphere MDM Algorithms V11 course prepares students to work with and customize the algorithm configurations deployed to the InfoSphere MDM Probabilistic Matching Engine (PME) for a Virtual and Physical MDM implementations. The PME is the heart of all Matching, Linking, and Searching for entities (Person, Organization, etc) that exist in InfoSphere MDM.

This course has a heavy emphasis on the exercises, where the students will implement the customization discussed in the course to perform matching, linking, and searching on fields not provided by the default implementation.

At the end of this course it is expected students will feel comfortable customizing an algorithm for the PME for a Virtual and Physical MDM implementations.

This course has a heavy emphasis on exercises and takes a participant through creating a process to search and update a customers address.  The search, get and update services are performed against the InfoSphere MDM.

If you are looking to get an introduction to how BPM and MDM can work together using the MDM Application Toolkit, then this course is for you.

This course has a heavy emphasis on exercises and takes a participant through creating a process to search and update a customer"s address. The search, get and update services are performed against the InfoSphere MDM.

If you are looking to get an introduction to how BPM and MDM can work together using the MDM Application Toolkit, then this course is for you.

The next courses that may be of interest to you include:

• Data Model and Service Mapping for the InfoSphere MDM Advanced Edition V10 (ZZ610)
• Customizing the InfoSphere MDM Advanced Edition V10 (ZZ640)
• IBM InfoSphere MDM Server Service Customization for MDM Server 9 (ZZ340)
• InfoSphere MDM Server User Interface Generator (DC560)

This course is designed for anyone who wants to get an understanding of the InfoSphere MDM Architecture (including the Virtual and Physical Hubs).  This course walks the students through the major components of the InfoSphere MDM and how each component interacts.  Students will learn how InfoSphere MDM responds once a service is invoked and the the various configuration and extension points of a service.  The course is used as an introduction to various components that make up the MDM Architecture and prepares the students to identify how MDM will fit into their organization and what pieces may be customized to fit their business requirements.

The next courses that may be of interest to you include:

• Data Model and Service Mapping for the InfoSphere MDM Advanced Edition V10 (ILO) (ZZ610)
• Customizing the InfoSphere MDM Advanced Edition V10 (ZZ640)
• InfoSphere MDM Server Service Customization for MDM Server 9 (ZZ340)
• InfoSphere MDM Server User Interface Generator (DC560)

This 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.

Do you want to find duplicates and perfect a search algorithm for your InfoSphere MDM Physical implementation? Then this course is designed for you. The InfoSphere MDM V11 Physical Module Algorithms course prepares you to work with and customize the algorithm configurations deployed to the InfoSphere MDM Probabilistic Matching Engine (PME) for the Physical MDM implementation.

This course has a heavy emphasis on the exercises, where you will deploy a new MDM configuration, invoke interactions, walk through the default matching algorithm, and create a custom handler and composite view.

At the end of this course, it is expected that you will feel comfortable implementating a new Virtual configuration data model, invoking interactions and creating customization to the Virtual MDM.

This course is designed for anyone who wants to get an understanding of how to use and customize the InfoSphere Master Data Management using the InfoSphere MDM Workbench. This course takes a you through the process customizing both the Virtual and Physical MDM using the InfoSphere MDM Workbench. The focus of the course is on the core features of the Workbench: Creating a Physical MDM Addition, creating a Physical MDM Extension, creating a Physical MDM Behavior Extension, creating a composite service, deploying a Virtual MDM configuration, configuring the Virtual Data Model, creating a Virtual custom Composite View, creating a Virtual Callout Handler, generating an enterprise service interface using the Virtual data model and customizing a Hybrid implementation. For each core area, the instructor will explain the high-level concepts, have them work with the feature and then demo and review the feature details. Heavy emphasis is put on exercises and activities, allowing them to apply the knowledge that they learn in the classroom, after course conclusion.

Contains: instructional and interactive content, demonstrations and hands-on simulated exercises.

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.

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 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.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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 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.

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.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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.

This coure is a prerequisite for the IX224 Database Administration course.  It introduces students to basic Informix terminology, system access, and data types.

This course gets you up and running with a set of procedures for analyzing time series data.  Learn how to forecast using a variety of models, including regression, exponential smoothing, and ARIMA, which take into account different combinations of trend and seasonality. The Expert Modeler features will be covered, which is designed to automatically select the best fitting exponential smoothing or ARIMA model, but you will also learn how to specify your own custom models, and also how to identify ARIMA models yourself using a variety of diagnostic tools such as time plots and autocorrelation plots.

This course is designed to introduce students to IBM Data Science Experience. The course covers how to create and set up a project and to be familiar with how to create, code, collaborate, and share notebooks while working with a variety of data sources to analyze data.

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course provides participants with a high level overview of the IBM Cognos Analytics suite of products and their underlying architecture. They will examine each component as it relates to an Analytics solution. Participants will be shown a range of resources to provide additional information on each product.

This course provides participants with a high level overview of the IBM Cognos Analytics suite of products and their underlying architecture. The participants will explore different components and examine how those components relate to an analytics solution.

Note: Guided eLearning is a self-paced offering which includes web-based content for self-study and videos (including audio) that demonstrate activities.

This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

This 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 C&R 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

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This 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 C&R 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.

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.

Teaches experienced DataStage developers how to use the Hierarchical Data stage to parse, compose, and transform XML data.

Note: Guided eLearning is a self-paced offering which includes web-based content for self study and videos (including audio) that demonstrate the hands-on activity.