Duration
14 hours (usually 2 days including breaks)
Requirements
Motivation to learn
Overview
Goal:
Learning to work with SPSS at the level of independence
The addressees:
Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques.
Course Outline
Using the program
- The dialog boxes
- input / downloading data
- the concept of variable and measuring scales
- preparing a database
- Generate tables and graphs
- formatting of the report
- Command language syntax
- automated analysis
- storage and modification procedures
- create their own analytical procedures
Data Analysis
- descriptive statistics
- Key terms: eg variable, hypothesis, statistical significance
- measures of central tendency
- measures of dispersion
- measures of central tendency
- standardization
- Introduction to research the relationships between variables
- correlational and experimental methods
- Summary: This case study and discussion
Duration
14 hours (usually 2 days including breaks)
Requirements
- No data mining background needed
Audience
- Data analysts
- Anyone who wants to learn about SPSS Modeler
Overview
IBM SPSS Modeler is a software used for data mining and text analytics. It provides a set of data mining tools that can build predictive models and perform data analytic tasks.
This instructor-led, live training (online or onsite) is aimed at data analysts or anyone who wishes to use SPSS Modeler to perform data mining activities.
By the end of this training, participants will be able to:
- Understand the fundamentals of data mining.
- Learn how to import and assess data quality with the Modeler.
- Develop, deploy, and evaluate data models efficiently.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
- Overview of advanced analytics and data mining
- Overview of CRISP-DM
- Understanding the Modeler UI
- Understanding the mechanics of building streams
Understanding Data
- Reading data into Modeler
- Measurement level and field roles
- Using the data audit node
Data Preparation
- Selecting cases
- Reclassifying categorical values
- Using append node and merge node
- Deriving fields
Modeling
- Overview of modeling
- Using a partition node
- Building a CHAID model
- Model assessment
Evaluation and Deployment
- Using analysis and evaluation node
- Scoring new data and exporting
- Using flat file node
Troubleshooting
Summary and Next Steps
