0A069G IBM SPSS Modeler Foundations (V18.2)
Duration: 2 Days
Level: Basic
Audience: Business Analyst, Data Scientist, Research
Next Sessions
| Start (YYYY-MM-DD) | End (YYYY-MM-DD) | Language | Amount | |
|---|---|---|---|---|
| 2024-05-09 | 2024-05-10 | English | 1450 EUR | |
| 2024-05-23 | 2024-05-24 | English | 1450 EUR | |
| 2024-06-06 | 2024-06-07 | English | 1450 EUR | |
| 2024-06-20 | 2024-06-21 | English | 1450 EUR | |
| 2024-07-04 | 2024-07-05 | English | 1450 EUR | |
| 2024-07-18 | 2024-07-19 | English | 1450 EUR | |
| 2024-08-01 | 2024-08-02 | English | 1450 EUR | |
| 2024-08-15 | 2024-08-16 | English | 1450 EUR | |
| 2024-08-29 | 2024-08-30 | English | 1450 EUR | |
| 2024-09-12 | 2024-09-13 | English | 1450 EUR | |
| 2024-09-26 | 2024-09-27 | English | 1450 EUR | |
| 2024-10-10 | 2024-10-11 | English | 1450 EUR | |
| 2024-10-24 | 2024-10-25 | English | 1450 EUR | |
| 2024-11-07 | 2024-11-08 | English | 1450 EUR | |
| 2024-11-21 | 2024-11-22 | English | 1450 EUR | |
| 2024-12-05 | 2024-12-06 | English | 1450 EUR | |
| 2024-12-19 | 2024-12-20 | English | 1450 EUR |
Overview
This course provides the foundations of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The course provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and introduces the student to modeling.
Prerequisites
- Knowledge of your business requirements
Introduction to IBM SPSS Modeler
• Introduction to data science
• Describe the CRISP-DM methodology
• Introduction to IBM SPSS Modeler
• Build models and apply them to new data
Collect initial data
• Describe field storage
• Describe field measurement level
• Import from various data formats
• Export to various data formats
Understand the data
• Audit the data
• Check for invalid values
• Take action for invalid values
• Define blanks
Set the unit of analysis
• Remove duplicates
• Aggregate data
• Transform nominal fields into flags
• Restructure data
Integrate data
• Append datasets
• Merge datasets
• Sample records
Transform fields
• Use the Control Language for Expression Manipulation
• Derive fields
• Reclassify fields
• Bin fields
Further field transformations
• Use functions
• Replace field values
• Transform distributions
Examine relationships
• Examine the relationship between two categorical fields
• Examine the relationship between a categorical and continuous field
• Examine the relationship between two continuous fields
Introduction to modeling
• Describe modeling objectives
• Create supervised models
• Create segmentation models
Improve 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
Introduction to IBM SPSS Modeler
• Introduction to data science
• Describe the CRISP-DM methodology
• Introduction to IBM SPSS Modeler
• Build models and apply them to new data
Collect initial data
• Describe field storage
• Describe field measurement level
• Import from various data formats
• Export to various data formats
Understand the data
• Audit the data
• Check for invalid values
• Take action for invalid values
• Define blanks
Set the unit of analysis
• Remove duplicates
• Aggregate data
• Transform nominal fields into flags
• Restructure data
Integrate data
• Append datasets
• Merge datasets
• Sample records
Transform fields
• Use the Control Language for Expression Manipulation
• Derive fields
• Reclassify fields
• Bin fields
Further field transformations
• Use functions
• Replace field values
• Transform distributions
Examine relationships
• Examine the relationship between two categorical fields
• Examine the relationship between a categorical and continuous field
• Examine the relationship between two continuous fields
Introduction to modeling
• Describe modeling objectives
• Create supervised models
• Create segmentation models
Improve 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