Training offering

iTLS Australia

Advanced Data Preparation Using IBM SPSS Modeler (v18.1.1)

Information

Length: 8.0 Hours
Course code: 0A058G
Delivery method: Classroom
Price: 950 AUD
This training is available on request.
Please contact us by phone or email at :
+ 61 (3) 853944858
info@flanegroup.com.au

Overview

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.

Public

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.

Prerequisits

• 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.
• Prior completion of the Introduction to IBM SPSS Modeler and Data Science course is recommended.

Objective

Please refer to course overview

Topics

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 field’s 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