name :
KM413G
title :
IBM InfoSphere Advanced QualityStage V11.5
category :
Cloud
vendor :
IBM
classroomDeliveryMethod :
Classroom IBM
descriptions :
description :
OverviewThis 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.AudienceThe intended audience for this course are:
• QualityStage programmers
• Data Analysts responsible for data quality using QualityStage
• Data Quality Architects
• Data Cleansing Developers
• Data Quality Developers needing to customize QualityStage rule setsPrerequisitesParticipants should have:
• Compled the QualityStage Essentials course, or have equivalent experience
• familiarity with Windows and a text editor
• familiarity with elementary statistics and probability concepts (desirable but not essential)ObjectivePlease refer to the course overviewCourse OutlineAfter completing this course,  you should be able to:
• Modify rule sets
• Build custom rule sets
• Standardize data using the custom rule set
• Perform a reference match using standardized data and a reference data set
• Use advanced techniques to refine a Two-source match
overview :
[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.]
abstract :
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.
prerequisits :
objective :
Overview 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. Audience The intended audience for this course are:
• QualityStage programmers
• Data Analysts responsible for data quality using QualityStage
• Data Quality Architects
• Data Cleansing Developers
• Data Quality Developers needing to customize QualityStage rule sets Prerequisites Participants should have:
• Compled the QualityStage Essentials course, or have equivalent experience
• familiarity with Windows and a text editor
• familiarity with elementary statistics and probability concepts (desirable but not essential) Objective Please refer to the course overview
topic :
Course OutlineAfter completing this course,  you should be able to:
• Modify rule sets
• Build custom rule sets
• Standardize data using the custom rule set
• Perform a reference match using standardized data and a reference data set
• Use advanced techniques to refine a Two-source match
startDate :
2021-05-10T17:52:53Z
endDate :
2022-04-15T00:00:00Z
lastModified :
2021-05-10T08:00:44Z
created :
2016-05-30T03:20:59Z
duration :
24
durationUnit :
HOURS
ibmIPType :
listPrice :
currency :
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