This course goes through the stages of a data science project from importing data to deployment, using services in Watson Studio and Watson Machine Learning for Cloud Pak for Data.
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This learning offering will tell a holistic story of Cloud Pak for Data including collaboration across an organization, which is key in this platform. Applicable to all personas. Multiple use cases will provide understanding of how organizations can benefit from Cloud Pak for Data. A variety of features will also be explored, providing students with the insight on how to use the platform. This learning is relevant for Cloud Pak for Data and Cloud Pak for Data System. This WBT contains instructional and interactive content, demonstrations and hands-on simulated exercises.
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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.
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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
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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.
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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.
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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
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This 0E069G: IBM SPSS Modeler Foundations v18.2 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.
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
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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.
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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.
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Master the art of text analytics with IBM SPSS Modeler: From Data to Insights in 1 course.
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 templates 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
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This 0G09BG: Advanced Statistical Analysis Using IBM SPSS Statistics v26 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.
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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.
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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.
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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
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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.
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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.
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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.
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This 1W730G: IBM Integrated Analytics System (IIAS) for Administrators v1.0 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.
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.
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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.
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.
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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.
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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.
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This 2M204G: IBM InfoSphere DataStage Essentials v11.5 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.
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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.
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This course is designed to introduce advanced parallel job development techniques in DataStage v11.5. In this course you will develop a deeper understanding of the DataStage architecture, including a deeper understanding of the DataStage development and runtime environments. This will enable you to design parallel jobs that are robust, less subject to errors, reusable, and optimized for better performance.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.
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This 2L464G: Db2 11.1 Advanced Database Administration course is designed to teach you how to:
- Perform advanced monitoring using the Db2 administrative views and routines in SQL queries.
- Manage the disk space assigned in Database Managed Storage (DMS) and Automatic Storage table spaces, including the activities of the rebalancer.
- Use SQL queries and Db2 commands to check the high water mark on table spaces and to monitor the rebalance operation.
- Utilize the REBUILD option of RESTORE, which can build a database copy with a subset of the tablespaces using database or tablespace backup images.
- Plan and execute the TRANSPORT option of RESTORE to copy schemas of objects between two Db2 databases.
- Create incremental database or tablespace level backups to reduce backup processing and backup image storage requirements.
- 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.
- Describe the various types of database memory including buffer pools, sort memory, lock memory and utility processing memory.
- Adjust database or Db2 instance configuration options to improve application performance or processing efficiency.
- Implement Db2 Self Tuning Memory management for specific database memory areas.
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.
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This course teaches you to perform, basic and advanced, database administrative tasks using Db2 11.1. 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 various db2pd command options. Students will learn how to implement automatic archival for database logs and how to plan a redirected database restore to relocate either selected table spaces or an entire database. The REBUILD option of RESTORE, which can build a database copy with a subset of the tablespaces, will be discussed. We will also cover using the TRANSPORT option of RESTORE to copy schemas of objects between two Db2 databases. The selection of indexes to improve application performance and the use of SQL statements to track database performance and health will be covered. This course provides a quick start to Db2 database administration skills for experienced relational Database Administrators (DBA).
The lab demonstrations are performed using DB2 LUW 11.1 for Linux. For some lab tasks, students will have the option to complete the task using a DB2 command line processor, or using the graphical interface provided by IBM Data Server Manager.
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.
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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.
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This course teaches how to build QualityStage parallel jobs that investigate, standardize, match, and consolidate data records. Students will gain experience by building an application that combines customer data from three source systems into a single master customer record.
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This course will step you through the QualityStage data cleansing process. You will transform an unstructured data source into a format suitable for loading into an existing data target. You will cleanse the source data by building a customer rule set that you create and use that rule set to standardize the data. You will next build a reference match to relate the cleansed source data to the existing target data.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.
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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.
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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.
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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.
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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.
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In this 2M803G: IBM Information Analyzer Essentials v11.5 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.
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This 2Z840G: InfoSphere MDM Workbench v11 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 participant 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 the participants work with the feature and then demo and review the feature details. Heavy emphasis is put on exercises and activities, allowing course participants to apply the knowledge that they learn in the classroom, after course conclusion.
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.
