Adfor S.p.a.

Introduction to IBM SPSS Modeler and Data Mining (v18) SPVC

Adfor S.p.a.
Online

500 
+IVA
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Tipologia Corso
Metodologia Online
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Descrizione

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 overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling. Objective Please refer to course overview.

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Dove e quando
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Online
Inizio Scegli data
Luogo
Online

Domande più frequenti

· A chi è diretto?

Anyone who wants to become familiar with IBM SPSS Modeler.

· Requisiti

General computer literacy.

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Cosa impari in questo corso?

Import
Data Mining

Programma

1: Introduction to data mining • List two applications of data mining • Explain the stages of the CRISP-DM process model • Describe successful data-mining projects and the reasons why projects fail • Describe the skills needed for data mining 2: Working with IBM SPSS Modeler • Describe the MODELER user-interface • Work with nodes • Run a stream or a part of a stream • Open and save a stream • Use the online Help 3: Creating a data-mining project • Explain the basic framework of a data-mining project • Build a model • Deploy a model 4: Collecting initial data • Explain the concepts "data structure", "unit of analysis", "field storage" and "field measurement level" • Import Microsoft Excel files • Import IBM SPSS Statistics files • Import text files • Import from databases • Export data to various formats 5: Understanding the data • Audit the data • Explain how to check for invalid values • Take action for invalid values • Explain how to define blanks 6: Setting the unit of analysis • Set the unit of analysis by removing duplicate records • Set the unit of analysis by aggregating records • Set the unit of analysis by expanding a categorical field into a series of flag fields 7: Integrating data • Integrate data by appending records from multiple datasets • Integrate data by merging fields from multiple datasets • Sample records 8: Deriving and reclassifying fields • Use the Control Language for Expression Manipulation (CLEM) • Derive new fields • Reclassify field values 9: Identifying relationships • Examine the relationship between two categorical fields • Examine the relationship between a categorical field and a continuous field • Examine the relationship between two continuous fields 10: Introduction to modeling • List three modeling objectives • Use a classification model • Use a segmentation model


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