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This course focuses on reviewing concepts of data science, where participants will learn the stages of a data science project. Topics include using automated tools to prepare data for analysis, build models, evaluate models, and deploy models. To learn about these data science concepts and topics, participants will use IBM SPSS Modeler as a tool.
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What You'll Learn

  • Introduction to data science and IBM SPSS Modeler
  • Setting measurement levels
  • Exploring the data
  • Using automated data preparation
  • Partitioning the data
  • Selecting predictors
  • Using automated modeling
  • Evaluating models
  • Deploying models

Who Should Attend

  • Business Analysts
  • Data Scientists
  • Participants who want to get started with data science
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Prerequisites

It is recommended that you have an understanding of your business data

Learning Journey

Coming Soon...

1: Introduction to data science and IBM SPSS Modeler

  • Explain the stages in a data-science project, using the CRISP-DM methodology
  • Create IBM SPSS Modeler streams
  • Build and apply a machine learning model

2: Setting measurement levels

  • Explain the concept of field measurement level
  • Explain the consequences of incorrect measurement levels
  • Modify a fields measurement level

3: Exploring the data

  • Audit the data
  • Check for invalid values
  • Take action for invalid values
  • Impute missing values
  • Replace outliers and extremes

4: Using automated data preparation

  • Automatically exclude low quality fields
  • Automatically replace missing values
  • Automatically replace outliers and extremes

5: Partitioning the data

  • Explain the rationale for partitioning the data
  • Partition the data into a training set and testing set

6: Selecting predictors

  • Automatically select important predictors (features) to predict a target
  • Explain the limitations of automatically selecting features

7: Using automated modeling

  • Find the best model for categorical targets
  • Find the best model for continuous targets
  • Explain what an ensemble model is

8: Evaluating models

  • Evaluate models for categorical targets
  • Evaluate models for continuous targets

9: Deploying models

  • List two ways to deploy models
  • Export scored data
This course is not associated with any Certification.

Frequently Asked Questions (FAQs)

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    IBM-certified professionals are in high demand, making them valuable assets to any organization seeking to leverage IBM's innovative solutions.

  • What to expect for the examination?

    IBM offers a variety of certification exams at different levels (Basic, Intermediate, and Advanced) covering various technologies and job roles.

    Exams typically consist of multiple-choice questions and may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by IBM from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is IBM certification valid for?

    The validity period of IBM certifications varies. Some certifications, particularly those focused on specific product versions or technologies, may have expiration dates.

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    Note: Certification requirements and policies may be updated by IBM from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

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Course Curriculum

Course Curriculum

Training Schedule

Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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