0A069G IBM SPSS Modeler Foundations (V18.2)
Duração: 2 Dias
Nível: Básico
Audiência: Business Analyst, Data Scientist, Research
PRÓXIMAS SESSÕES
| Início (AAAA-MM-DD) | Fim (AAAA-MM-DD) | Língua | Preço | |
|---|---|---|---|---|
| 2023-11-09 | 2023-11-10 | Português | 1450 EUR | |
| 2023-11-23 | 2023-11-24 | Português | 1450 EUR | |
| 2023-12-07 | 2023-12-08 | Português | 1450 EUR | |
| 2023-12-21 | 2023-12-22 | Português | 1450 EUR | |
| 2024-01-18 | 2024-01-19 | Português | 1450 EUR | |
| 2024-02-01 | 2024-02-02 | Português | 1450 EUR | |
| 2024-02-15 | 2024-02-16 | Português | 1450 EUR | |
| 2024-02-29 | 2024-03-01 | Português | 1450 EUR | |
| 2024-03-14 | 2024-03-15 | Português | 1450 EUR | |
| 2024-03-28 | 2024-03-29 | Português | 1450 EUR | |
| 2024-04-11 | 2024-04-12 | Português | 1450 EUR | |
| 2024-04-25 | 2024-04-26 | Português | 1450 EUR | |
| 2024-05-09 | 2024-05-10 | Português | 1450 EUR | |
| 2024-05-23 | 2024-05-24 | Português | 1450 EUR | |
| 2024-06-06 | 2024-06-07 | Português | 1450 EUR | |
| 2024-06-20 | 2024-06-21 | Português | 1450 EUR |
SÍNTESE
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.
PREREQUISITOS
- 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