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0A008G – Introduction to IBM SPSS Modeler and Data Science v18.1.1

Home / 0A008G – Introduction to IBM SPSS Modeler and Data Science v18.1.1

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 structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.

Course Outline:

1. 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 of the 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

Course Audience & Prerequisites:

Audience 

Anyone who wants to become familiar with IBM SPSS Modeler

Prerequisites

General computer literacy.

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

1. 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 of the 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

Audience 

Anyone who wants to become familiar with IBM SPSS Modeler

Prerequisites

General computer literacy.

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.

0A108G – Introduction to IBM SPSS Modeler Text Analytics (V18.1.1)

This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v18)) teaches you how to analyze text data using IBM SPSS Modeler Text Analytics. Students will be introduced to the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform churn analysis in telecommunications. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource templates and Text Analysis packages to share with other projects and other users.

0A058G – Advanced Data Preparation Using IBM SPSS Modeler

This course covers advanced topics to aid in the preparation of data for a successful data science project. Students will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.

0A0U8G – Predictive Modeling for Categorical Targets Using IBM SPSS Modeler

This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

0A0V8G – Predictive Modeling for Continuous Targets Using IBM SPSS Modeler

This course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.

0A048G – Clustering and Association Modeling Using IBM SPSS Modeler

Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.

0A038G – Advanced Predictive Modeling Using IBM SPSS Modeler

This course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.