This coure is a prerequisite for the IX224 Database Administration course. It introduces students to basic Informix terminology, system access, and data types.
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 database administrators how to plan, implement and manage Db2 11.1 databases using the High Availability Disaster Recovery HADR) feature. The lectures cover the processing performed for a Db2 Primary and Standby Db2 database. The Db2 database configuration options that define and control the HADR function are covered. The option to define and operate multiple HADR standby databases will be explained. The course also covers the special considerations for allowing read only access by applications to a HADR Standby database. Students will learn the Db2 commands like TAKEOVER, START HADR and STOP HADR that are used to control HADR primary and standby database activity. The monitoring for HADR status of the primary and standby databases using the db2pd commands will be presented. The course also presents usage of HADR with Db2 pureScale databases.
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 J1222G: IBM Cognos Controller: Author Reports 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.
This 2E121G: SQL Workshop course provides an introduction to the SQL language. This course is appropriate for those working in all DB2 environments, but particularly for, z/OS, 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 2M510G: IBM InfoSphere Information Server 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 2M520G: IBM InfoSphere DataStage Engine Administration for Information Server v11.5 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 J4098G: IBM Cognos Analytics – Author Active Reports v11.0 course provides participants with an understanding of Active Report content and functionality within IBM Cognos Analytics – Reporting. Through lecture, demonstrations, and exercises, participants increase their IBM Cognos Analytics experience by building highly interactive reports using Active Report controls, which can then be distributed to and consumed by users in a disconnected environment, including on mobile devices.
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 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 an understanding of Active Report content and functionality within IBM Cognos Analytics – Reporting. Through lecture, demonstrations, and exercises, participants increase their IBM Cognos Analytics experience by building highly interactive reports using Active Report controls, which can then be distributed to and consumed by users in a disconnected environment, including on mobile devices.
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.
The 2L451G: DB2 11 BLU Acceleration Implementation and Use course is intended for Data Administrators that need to prepare for using the DB2 BLU Acceleration facilities of DB2 11.1 for Linux, UNIX and Windows systems. The concepts and facilities of the BLU Acceleration feature of DB2 11 are presented including loading data into column-organized tables and monitoring the processing of SQL statements that access the tables.
The DB2 10.5 Fix Pack 4, referred to as Cancun, added support for Shadow tables, a new type of Materialized Query Table, and also Column-organized User Maintained MQT tables. One lecture unit describes these features. A demonstration allows students to implement and experiment with these functions. With DB2 11.1, BLU Acceleration can be used in a clustered multiple database partition DB2 environment.
This course includes a lecture and demonstration that allows students to create a set of column-organized tables from an existing set of row-organized tables and execute and analyze the performance of BLU Acceleration in a MPP database. The lab demonstrations are performed using DB2 LUW 11.1 for Linux.
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 2L464G: Db2 11.1 Advanced Database Administration course is designed to teach you how to:
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.
Do you want to find match member records, link member records, and perfect a search algorithm for your InfoSphere MDM Virtual implementation? Then this course is designed for you. The InfoSphere MDM Virtual Module 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 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 implementations.
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.
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.
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 guide report authors in building on their expertise with IBM Cognos Analytics by applying dimensional techniques to reports. Through interactive demonstrations and exercises, participants will learn how to author reports that navigate and manipulate dimensional data structures using the specific dimensional functions and features available in IBM Cognos Analytics.
The ZZ981G: InfoSphere MDM Physical Module Algorithms v11 course prepares you to work with and customize the algorithm configurations deployed to the InfoShere MDM Probabilistic Matching Engine (PME) for the Physical MDM implementation.
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This course is designed to guide report authors in building on their expertise with IBM Cognos Analytics by applying dimensional techniques to reports. Through interactive demonstrations and exercises, participants will learn how to author reports that navigate and manipulate dimensional data structures using the specific dimensional functions and features available in IBM Cognos Analytics.
Do you want to find match member records, link member records, and perfect a search algorithm for your InfoSphere MDM Virtual implementation?
Then this course is designed for you. The InfoSphere MDM Virtual Module Algorithms V.11 course prepares students to work with and customize the algorithm configurations deployed to the InfoSphere MDM Probabilistic Matching Engine (PME) for Virtual 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 that students will feel comfortable customizing an algorithm for the PME for Virtual implementations.
The InfoSphere MDM Application Toolkit course introduces students to the Business Process Manager (BPM) and the Process Designer to create processes that will use MDM data and services.
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 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.
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.
This course focuses on reviewing concepts of data science, where participants will learn the stages of a data science project. Topics include using automated tools to prepare data for analysis, build models, evaluate models, and deploy models. To learn about these data science concepts and topics, participants will use IBM SPSS Modeler as a tool.
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 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.
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 0E038G: Advanced Predictive Modeling Using IBM SPSS Modeler v18.1.1 course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.
This 0E039G: Advanced Machine Learning Models Using IBM SPSS Modeler v18.2 course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.
Clustering and Association Modeling Using IBM SPSS Modeler (v18.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.
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 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.
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 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.
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 2M700G: IBM BigIntegrate for Data Engineers v11.5.0.2 course teaches data engineers how to run DataStage jobs in a Hadoop environment. You will run jobs in traditional and YARN mode, access HDFS files and Hive tables using different file formats and connector stages.
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.
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.
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 2M625G: IBM InfoSphere Information Governance Catalog 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.
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.
Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.
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.
This course covers advanced topics to aid in the preparation of data for a successful data science project. You will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.
This course focuses on reviewing concepts of data science, where participants will learn the stages of a data science project. Topics include using automated tools to prepare data for analysis, build models, evaluate models, and deploy models. To learn about these data science concepts and topics, participants will use IBM SPSS Modeler as a tool.
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 auto correlation plots .
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