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Advanced Data Preparation Using IBM SPSS Modeler V18.1.1 (0A058G)

Overview

Duration: 1 day

This course covers advanced topics to aid in the preparation of data for a successful data science 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.

Objectives

Please refer to course overview

Content

1: Using functions to cleanse and enrich data

Use date functions

Use conversion functions

Use string functions

Use statistical functions

Use missing value functions

2: Using additional field transformations

Replace values with the Filler node

Recode continuous fields with the Binning node

Change a fields distribution with the Transform node

3: Working with sequence data

Use sequence functions

Count an event across records

Expand a continuous field into a series of continuous fields with the Restructure node

Use geospatial and time data with the Space-Time-Boxes node

4: Sampling, partitioning and balancing data

Draw simple and complex samples with the Sample node

Create a training set and testing set with the Partition node

Reduce or boost the number of records with the Balance node

5: Improving efficiency

Use database scalability by SQL pushback

Process outliers and missing values with the Data Audit node

Use the Set Globals node

Use parameters

Use looping and conditional execution

Audience

This advanced course is intended for anyone who wants to become familiar with the full range of techniques available in IBM SPSS Modeler for data preparation.

Prerequisites

Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and basic knowledge of modeling.