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
Requirements
- No data mining background needed
Audience
- Data analysts
- Anyone who wants to learn about SPSS Modeler
Testimonials (8)
Very useful in because it helps me understand what we can do with the data in our context. It will also help me
Nicolas NEMORIN - Adecco Groupe France
Course - KNIME Analytics Platform for BI
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan - NBrown Group
Course - From Data to Decision with Big Data and Predictive Analytics
Very tailored to needs.
Yashan Wang
Course - Data Mining with R
how the trainor shows his knowledge in the subject he's teachign
john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course - Data Vault: Building a Scalable Data Warehouse
I enjoyed the good real world examples, reviews of existing reports.
Ronald Parrish
Course - Data Visualization
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course - Data Mining & Machine Learning with R
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Course - Data Science for Big Data Analytics
I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.