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IBM: Introduction to IBM SPSS Modeler and Data Mining
Your data can reveal useful trends when exploration is structured with SPSS Modeler. With IBM SPSS, structure information preparation and exploration to interpret results more effectively. Develop an analytical practice that helps you support your conclusions and identify the limits of the methods.
- Duration
- 2 days 14 hours
- Code
- OA004 Code
Presentation
Introduction to IBM SPSS Modeler and Data Mining is a foundational course providing an overview of data mining and the basics of using IBM SPSS Modeler.
Data mining principles and practice are demonstrated through the CRISP-DM methodology.
Objectives
The course provides basic training in how to:
- Read, explore and manipulate data with IBM SPSS Modeler.
- Then create and use successful models.
Program
- Explain the steps in the CRISP-DM modelling process.
- Describe successful data mining projects and explain why a project may fail.
- Describe the skills and prerequisites needed for data mining.
- Describe the different areas of the Modeler user interface.
- Work with nodes and SuperNodes.
- Open, run and save a stream.
- Access the help function within Modeler.
- Explain the main basic concepts used in data mining.
- Build, evaluate and deploy a model.
- Use Sort and Filter nodes.
- Explain the concepts of data structure, records, fields, unit of analysis and storage.
- Read data from multiple file formats and export it in multiple formats.
- Examine distributions of categorical and continuous fields.
- Explain the most common ways to handle missing data.
- Data validation in Modeler.
- Remove duplicate records.
- Aggregate data: develop aggregation criteria.
- Append records from multiple databases into one dataset.
- Add fields from multiple datasets into one dataset.
- Use sampling for testing purposes.
- Use CLEM to transform data.
- Use the Derive node to create a new field.
- Use the Reclassify node.
- Use the Reorder node to rearrange fields.
- Examine the relationship between two categorical fields.
- Examine the relationship between two continuous fields.
- Examine the relationship between a continuous field and a categorical field.
- Modelling objectives.
- Introduction to classification.
- Introduction to segmentation.
Audience
This foundational course is intended for:
- IBM SPSS Modeler users with little or no experience.
- Anyone, with or without experience, wishing to master data mining.
- Anyone considering purchasing IBM SPSS Modeler.
Prerequisites
Only general computer knowledge is required. Statistical experience is not necessary.
Knowledge of the organisation of company data and the strategic issues surrounding the use of data mining is recommended.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.