Advanced Data Preparation Using IBM SPSS Modeler (V16) Eğitimi

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IBM  » IBM SPSS Modeler Eğitimler
Business Analysis  » Business Analysis Eğitimler

Advanced Data Preparation Using IBM SPSS Modeler (V16) (0A055G)

IBM Course Code: 0A055G

Advanced Data Preparation Using IBM SPSS Modeler (V16) covers advanced topics to aid in the preparation of data for a successful data mining 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.

 

 

Who Needs to Attend

This advanced course is for IBM SPSS Modeler Analysts and IBM SPSS Modeler Data Experts who want to become familiar with the full range of techniques available in IBM SPSS Modeler for data manipulation.

Prerequisites

You should have:

 

  • General computer literacy.
  • Some experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, and doing simple data exploration and manipulation using the Derive node.
  • Prior completion of Introduction to IBM SPSS Modeler and Data Mining (V16) is recommended.

Follow-On Courses

There are no follow-ons for this course.

 

Course Outline

Using Functions

  • Use date functions
  • Use conversion functions
  • Use string functions
  • Use statistical functions
  • Use missing value functions

Data Transformations

  • Use the Filler node to replace values
  • Use the Binning node to recode continuous fields
  • Use the Transform node to change a field's distribution

Working with Sequence Data

  • Use cross-record functions
  • Use the Count mode in the Derive node
  • Use the Restructure node to expand a continuous field into a series of continuous fields
  • Use the Space-Time-Boxes node to work with geospatial and time data

Sampling Records

  • Use the Sample node to draw simple and complex samples
  • Draw complex samples
  • Partition the data into a training and a testing set
  • Reduce or boost the number of records

Improving Efficiency

  • Use database scalability by SQL pushback
  • Use the Data Audit node to process outliers and missing values
  • Use the Set Globals node
  • Use parameters
  • Use looping and conditional execution

 

 

 



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