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Course Description
SPSS Modeler is one of the add-on modules on IBM Cloud Pak for Data. This course reviews the basics of how to import, explore, and prepare data, and introduces the student to machine learning models with SPSS Modeler on Cloud Pak for Data. This training also applies to IBM Cloud Pak for Data System and IBM Cloud Private for Data.
Objectives
Unit 1: Introduction to SPSS Modeler on IBM Cloud Pak for Data
- Introduction to data science
- Describe the CRISP-DM methodology
- Introduction to SPSS Modeler
- Build and deploy models
Unit 2: Import and explore the data
- Describe key terms in working with data
- Import and export data
- Audit the data
- Check for invalid values
- Define blank values
Unit 3: Integrate data
- Identify the unit of analysis
- De-duplicate, aggregate, create flag fields, transpose data
- Append and merge datasets
- Append datasets with incomplete data
- Merge datasets with incomplete data
Unit 4: Transform fields
- Use the Control Language for Expression Manipulation
- Derive fields
- Reclassify fields
- Bin fields
- Fill fields
Unit 5: Identify relationships
- Overview of the nodes to use
- Explore the relationship between two categorical fields
- Explore the relationship between a categorical field and a continuous field
- Explore the relationship between two continuous fields
Unit 6: Introduction to modeling
- Identify three types of machine learning models
- Identify three types of supervised models
- Identify unsupervised models
- Deploy machine learning models
Audience
Clients who are new to IBM SPSS Modeler on IBM Cloud Pak for Data or who want to find out more about using it.
Prerequisites
Knowledge of your business requirements
Content
Unit 1: Introduction to SPSS Modeler on IBM Cloud Pak for Data
- Introduction to data science
- Describe the CRISP-DM methodology
- Introduction to SPSS Modeler
- Build and deploy models
Unit 2: Import and explore the data
- Describe key terms in working with data
- Import and export data
- Audit the data
- Check for invalid values
- Define blank values
Unit 3: Integrate data
- Identify the unit of analysis
- De-duplicate, aggregate, create flag fields, transpose data
- Append and merge datasets
- Append datasets with incomplete data
- Merge datasets with incomplete data
Unit 4: Transform fields
- Use the Control Language for Expression Manipulation
- Derive fields
- Reclassify fields
- Bin fields
- Fill fields
Unit 5: Identify relationships
- Overview of the nodes to use
- Explore the relationship between two categorical fields
- Explore the relationship between a categorical field and a continuous field
- Explore the relationship between two continuous fields
Unit 6: Introduction to modeling
- Identify three types of machine learning models
- Identify three types of supervised models
- Identify unsupervised models
- Deploy machine learning models
For any further queries, feel free to contact one of the top software training institutes in bangalore - Global Knowledge Technologies.