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Description

IBM SPSS Modeler is a data mining workbench that helps you build predictive models quickly and intuitively, without programming. Analysts typically use SPSS Modeler to analyze data by doing data mining and then deploying models.


Overview:
This course introduces students to data mining and to the functionality available within IBM SPSS Modeler. The series of stand-alone videos, are designed to introduce students to specific nodes or data mining topics. Each video consists of detailed instructions explaining why we are using a technique, in what situations it is used, how to set it up, and how to interpret the results. This course is broken up into phases. The Introduction to Data Mining Phase is designed to get you up to speed on the idea of data mining. You will also learn about the CRISP-DM methodology which will serve as a guide throughout the course and you will also learn how to navigate within Modeler. The Data Understanding Phase addresses the need to understand what your data resources are and the characteristics of those resources. We will discuss how to read data into Modeler. We will also focus on describing, exploring, and assessing data quality. The Data Preparation Phase discusses how to integrate and construct data. While the Modeling Phase will focus on building a predictive model. The Evaluation Phase focuses how to take your data mining results so that you can achieve your business objectives. And finally the Deployment Phase allows you to do something with your findings.
Who this course is for:
This course is for anyone that would like to learn how to use IBM SPSS Modeler.
This course is for anyone that would like to learn how to do Data Mining.

What you'll learn

Data Mining and Advanced Analytics Defined

Modeling Methods in Modeler

CRISP-DM Overview

General Modeler Orientation

Reading Data

Assessing Data Quality

Integrating Data

Constructing Data

Modeling

Evaluation

Deployment

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction to Data Mining
5
1.1-What is Advanced Analytics and Data Mining?
1.2-Modeling Methods
1.3-What is CRISP-DM?
1.4-Modeler's Workbench Design
1.5-General Modeler Orientation
2-Data Understanding
5
2.1-Var. File Node
2.2-Excel Node
2.3-Type Node
2.4-Data Audit Node
2.5-Data Audit Node: Quality Tab
3-Data Preparation
6
3.1-Derive Node: Formula
3.2-Derive Node: Flag
3.3-Derive Node: Nominal
3.4-Derive Node: Conditional
3.5-Append Node
3.6-Merge Node
4-Modeling
3
4.1-Partition Node
4.2-CHAID
4.3-CHAID Demo
5-Evaluation
2
5.1-Analysis Node
5.2-Evaluation Node
6-Deployment
1
6.1-Flat File Node