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6X140G – IBM SPSS Modeler Foundations on IBM Cloud Pak for Data (V2.1.X) – (SPVC)

Home / 6X140G – IBM SPSS Modeler Foundations on IBM Cloud Pak for Data (V2.1.X) – (SPVC)

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.

Course Outline:

Course Outline:

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

Course Audience & Prerequisites:

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.

Course Offerings:

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. 

Please note that there is no live interaction with an instructor in this format.

  • Course Outline
  • Course Audience & Prerequisites
  • Course Offerings
  • Related Courses

Course Outline:

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.

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. 

Please note that there is no live interaction with an instructor in this format.