name :

0A008G

title :

Introduction to IBM SPSS Modeler and Data Science (v18.1.1)

RegistrationLink :

https://academy.techdata.com/on-demand/TT-143235

category :

Analytics

vendor :

IBM

classroomDeliveryMethod :

Classroom IBM

descriptions :

description :

OverviewThis course provides the fundamentals 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 v18.1.1, and introduces the student to modeling.Audience• Business analysts

• Data scientists

• Clients who are new to IBM SPSS Modeler or want to find out more about using itPrerequisites• It is recommended that you have an understanding of your business dataObjective

• List two applications of data science

• Explain the stages in the CRISP-DM methodology

• Describe the skills needed for data science

2. Introduction to IBM SPSS Modeler

• Describe IBM SPSS Modeler's user-interface

• Work with nodes and streams

• Generate nodes from output

• Use SuperNodes

• Execute streams

• Open and save streams

• Use Help

3. Introduction to data science using IBM SPSS Modeler

• Explain the basic framework of a data-science project

• Build a model

• Deploy a model

4. Collecting initial data

• Explain the concepts "data structure", "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

• Check for invalid values

• Take action for invalid values

• Define blanks

6. Setting the of analysis

• Remove duplicate records

• Aggregate records

• Expand a categorical field into a series of flag fields

• Transpose data

7. Integrating data

• Append records from multiple datasets

• Merge 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 types of models

• Use a supervised model

• Use a segmentation model

• Data scientists

• Clients who are new to IBM SPSS Modeler or want to find out more about using itPrerequisites• It is recommended that you have an understanding of your business dataObjective

- Introduction to data science
- Introduction to IBM SPSS Modeler
- Introduction to data science using IBM SPSS Modeler
- Collecting initial data
- Understanding the data
- Setting the of analysis
- Integrating data
- Deriving and reclassifying fields
- Identifying relationships
- Introduction to modeling

• List two applications of data science

• Explain the stages in the CRISP-DM methodology

• Describe the skills needed for data science

2. Introduction to IBM SPSS Modeler

• Describe IBM SPSS Modeler's user-interface

• Work with nodes and streams

• Generate nodes from output

• Use SuperNodes

• Execute streams

• Open and save streams

• Use Help

3. Introduction to data science using IBM SPSS Modeler

• Explain the basic framework of a data-science project

• Build a model

• Deploy a model

4. Collecting initial data

• Explain the concepts "data structure", "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

• Check for invalid values

• Take action for invalid values

• Define blanks

6. Setting the of analysis

• Remove duplicate records

• Aggregate records

• Expand a categorical field into a series of flag fields

• Transpose data

7. Integrating data

• Append records from multiple datasets

• Merge 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 types of models

• Use a supervised model

• Use a segmentation model

overview :

[This course provides the fundamentals 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 v18.1.1, and introduces the student to modeling.]

abstract :

This course provides the fundamentals 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 v18.1.1, and introduces the student to modeling.

prerequisits :

objective :

Overview
This course provides the fundamentals 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 v18.1.1, and introduces the student to modeling.
Audience
• Business analysts

• Data scientists

• Clients who are new to IBM SPSS Modeler or want to find out more about using it Prerequisites • It is recommended that you have an understanding of your business data Objective

• Data scientists

• Clients who are new to IBM SPSS Modeler or want to find out more about using it Prerequisites • It is recommended that you have an understanding of your business data Objective

- Introduction to data science
- Introduction to IBM SPSS Modeler
- Introduction to data science using IBM SPSS Modeler
- Collecting initial data
- Understanding the data
- Setting the of analysis
- Integrating data
- Deriving and reclassifying fields
- Identifying relationships
- Introduction to modeling

topic :

Course Outline1. Introduction to data science

• List two applications of data science

• Explain the stages in the CRISP-DM methodology

• Describe the skills needed for data science

2. Introduction to IBM SPSS Modeler

• Describe IBM SPSS Modeler's user-interface

• Work with nodes and streams

• Generate nodes from output

• Use SuperNodes

• Execute streams

• Open and save streams

• Use Help

3. Introduction to data science using IBM SPSS Modeler

• Explain the basic framework of a data-science project

• Build a model

• Deploy a model

4. Collecting initial data

• Explain the concepts "data structure", "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

• Check for invalid values

• Take action for invalid values

• Define blanks

6. Setting the of analysis

• Remove duplicate records

• Aggregate records

• Expand a categorical field into a series of flag fields

• Transpose data

7. Integrating data

• Append records from multiple datasets

• Merge 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 types of models

• Use a supervised model

• Use a segmentation model

• List two applications of data science

• Explain the stages in the CRISP-DM methodology

• Describe the skills needed for data science

2. Introduction to IBM SPSS Modeler

• Describe IBM SPSS Modeler's user-interface

• Work with nodes and streams

• Generate nodes from output

• Use SuperNodes

• Execute streams

• Open and save streams

• Use Help

3. Introduction to data science using IBM SPSS Modeler

• Explain the basic framework of a data-science project

• Build a model

• Deploy a model

4. Collecting initial data

• Explain the concepts "data structure", "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

• Check for invalid values

• Take action for invalid values

• Define blanks

6. Setting the of analysis

• Remove duplicate records

• Aggregate records

• Expand a categorical field into a series of flag fields

• Transpose data

7. Integrating data

• Append records from multiple datasets

• Merge 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 types of models

• Use a supervised model

• Use a segmentation model

startDate :

2021-01-22T04:21:14Z

endDate :

2022-04-15T00:00:00Z

lastModified :

2021-01-20T08:00:39Z

created :

2017-12-21T08:00:29Z

duration :

16

durationUnit :

HOURS

ibmIPType :

listPrice :

currency :

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