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 Web-Based Training course teaches authors how to create dashboards in IBM Cognos Analytics so users can explore and interact with their data and gain insight into their business. You will learn how to add data sources, create and interact with dashboards, and customize content for presentation. You will also learn how to create effective narratives by using stories, and how to use explorations to perform a deeper analysis on your data.

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 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.

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 application developers how to set up a Controller application and effectively use Controller in their organization’s consolidation process. Students will also design and generate financial reports using Controller. Through a series of lectures and hands-on exercises, students will set up a Controller application by creating the necessary structures (such as accounts and companies), and then test the application to ensure that it works properly. Students will also learn how to work with currency translation, allocations, intercompany transactions, investments in subsidiaries, advanced formula calculations, and user-defined business rules, as well as define configuration settings and user access to the application.

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 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.

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 is designed to teach analysts how to use IBM Planning Analytics to analyze data to discover trends and exceptions, create and customize reports and templates, and contribute data to plans. Through a series of lectures and hands-on activities, you will learn how use Planning Analytics Workspace and Planning Analytics for Microsoft Excel to create analyses, enter data, create custom views and dashboards, and build formatted reports and forms.

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 analysts how to use IBM Planning Analytics to analyze data to discover trends and exceptions, create and customize reports and templates, and contribute data to plans. Through a series of lectures and hands-on activities, you will learn how use Planning Analytics Workspace and Planning Analytics for Microsoft Excel to create analyses, enter data, create custom views and dashboards, and build formatted reports and forms.

This course is designed to teach modelers how to build a complete model in IBM Planning Analytics using Planning Analytics Workspace. Through a series of lectures and hands-on exercises, students will learn how to set up dimensions and cubes, manually enter data into these structures, and define the data that users can see. Students will also learn how to transfer data into the IBM Planning Analytics model, including the use of TurboIntegrator scripts to perform data transfer. In addition, the course outlines how to customize drill paths, convert currencies, and model for different fiscal requirements.

This course is designed to teach modelers how to build a complete model in IBM Planning Analytics using Planning Analytics Workspace. Through a series of lectures and hands-on exercises, students will learn how to set up dimensions and cubes, manually enter data into these structures, and define the data that users can see. Students will also learn how to transfer data into the IBM Planning Analytics model, including the use of TurboIntegrator scripts to perform data transfer. In addition, the course outlines how to customize drill paths, convert currencies, and model for different fiscal requirements.

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.

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. 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 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.

This 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.

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 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.

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 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.

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.

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, 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.

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 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.