Duration
2 days
Overview
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.
Audience
Anyone who wants to become familiar with IBM SPSS Modeler.
Prerequisites
General computer literacy.
Objectives
Please refer to course overview.
Key topics
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
Programme scope, delivery arrangements and suitability are confirmed during enquiry.