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0A018G – IBM Data science without a Ph.D. Using IBM SPSS Modeler (v18.1.1)

Home / 0A018G – IBM Data science without a Ph.D. Using IBM SPSS Modeler (v18.1.1)

This course focuses on reviewing concepts of data science, where students 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, students will use IBM SPSS Modeler as a tool.

Course Outline:

Course Outline

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 field's 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

Course Audience & Prerequisites:

Audience

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

Prerequisites

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

Course Offerings:

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

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

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 field's 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

Audience

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

Prerequisites

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

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

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.