IBM SPSS Schulungen finden - Das passende Seminar in Ihrer Nähe

Overview

In this course, students will learn to use scientific methods, processes, and algorithms to identify patterns, trends, and insights from data - with the goal of enabling better decision-making.  Students will use watsonx.ai to synthesize skills from statistics, computer science, and management information systems to analyze and interpret complex datasets. This course includes a practical component with hands-on exercises focused on collecting, preparing, mining, and visualizing data and then building a machine learning and foundation model with methods on how to deploy them. 

Overview

This course provides technical professionals with the skills that are needed to administer IBM MQ queue managers on distributed operating systems and in the Cloud. In addition to the instructor-led lectures, you participate in hands-on lab exercises that are designed to reinforce lecture content. The lab exercises use IBM MQ V9.0, giving you practical experience with tasks such as handling queue recovery, implementing security, and problem determination.

Note: This course does not cover any of the features of MQ for z/OS or MQ for IBM i.

Overview

This course provides technical professionals with the skills that are needed to administer IBM MQ queue managers on distributed operating systems and in the Cloud. In addition to the instructor-led lectures, you participate in hands-on lab exercises that are designed to reinforce lecture content. The lab exercises use IBM MQ V9.0, giving you practical experience with tasks such as handling queue recovery, implementing security, and problem determination.

Note: This course does not cover any of the features of MQ for z/OS or MQ for IBM i.

Overview

Plan and implement IBM SAN Volume Controller in your data center!

 

After completing this course, you should be able to:

 

• Distinguish the concepts of IBM Spectrum virtualization
• Recall the history for IBM SAN Volume Controller
• Classify the characteristics and components of the IBM SAN Volume Controller system and SAS attached expansion enclosures
• Outline setups required to integrate an SVC system solution
• Summarize the SVC systems’ ability to scale for capacity and performance
• Summarize the virtualization process converting physical storage space into virtual resources
• Recall the process to create host access storage on an SVC system
• Differentiate the advanced software features designed to simplify data management, reclaim storage  space, and preserve storage investments
• Differentiate methods in which to migrate data to and from the virtualized system environment
• Summarize the methods of remote data replications to improve availability and support for disaster recovery
• Employ administrative operations to manage, monitor, and troubleshoot the system environment
• Summarize the characteristics of IBM Storage Insights’ ability to identify, troubleshoot and minimize potential system downtime
• Summarize 3-Site Replication and Safeguarded Copy

 

 

Sie haben eine Einführung in das Thema Data Science, Data Analytics erhalten und können mit KNIME Daten extrahieren, transformieren, laden und visualisieren. Sie erhalten einen Data Science - Überblick und kennen wesentliche Konzepte und Begriffe rund um das Thema Data Science und Co.

Sie haben eine Einführung in das Thema Data Science, Data Analytics erhalten und können mit KNIME Daten extrahieren, transformieren, laden und visualisieren. Sie erhalten einen Data Science - Überblick und kennen wesentliche Konzepte und Begriffe rund um das Thema Data Science und Co.

Overview

This course equips experienced database administrators with the skills needed to analyze, tune, and optimize the performance of IBM Db2 12 for Linux, UNIX, and Windows. 

Participants will learn how to interpret and influence the Db2 query optimization process using statistical metadata, develop efficient indexing strategies for high-performance workloads, and apply advanced tools such as EXPLAIN, db2pd, and the Db2 Data Management Console to evaluate and refine SQL access paths. 

The course also introduces the Db2 AI Optimizer and automation techniques that simplify statistics collection and performance diagnostics. Emphasis is placed on real-world use cases, practical monitoring methods, and proactive tuning practices to ensure scalable and efficient database operations in enterprise environments.

Overview

This course equips experienced database administrators with the skills needed to analyze, tune, and optimize the performance of IBM Db2 12 for Linux, UNIX, and Windows. 

Participants will learn how to interpret and influence the Db2 query optimization process using statistical metadata, develop efficient indexing strategies for high-performance workloads, and apply advanced tools such as EXPLAIN, db2pd, and the Db2 Data Management Console to evaluate and refine SQL access paths. 

The course also introduces the Db2 AI Optimizer and automation techniques that simplify statistics collection and performance diagnostics. Emphasis is placed on real-world use cases, practical monitoring methods, and proactive tuning practices to ensure scalable and efficient database operations in enterprise environments.

The goal of the 3-day training course in Data Science and Data Analytics  with KNIME is to equip participants with the knowledge and skills  needed to effectively use KNIME for data preparation, visualization,  modeling, and reporting. By the end of the course, participants should  be able to design and implement data workflows using KNIME, apply  machine learning techniques to solve analytical problems, and automate  and deploy workflows using KNIME Server. The course aims to provide a  comprehensive understanding of the concepts, techniques, and tools used  in Data Science and Data Analytics using KNIME.

The goal of the 3-day training course in Data Science and Data Analytics  with KNIME is to equip participants with the knowledge and skills  needed to effectively use KNIME for data preparation, visualization,  modeling, and reporting. By the end of the course, participants should  be able to design and implement data workflows using KNIME, apply  machine learning techniques to solve analytical problems, and automate  and deploy workflows using KNIME Server. The course aims to provide a  comprehensive understanding of the concepts, techniques, and tools used  in Data Science and Data Analytics using KNIME.