Översikt
| Kurskod | 0E032SE | Leveranstyp | Self-paced Virtual Class |
|---|---|---|---|
| Kurslängd | 3.0 dagar | Kurstyp | |
| Listpris |
SEK 13 050,00 exkl moms
SEK 16 312,50 inkl moms |
PLEASE DO NOT MAKE TRAVEL ARRANGEMENT FOR THIS SELF PACED VIRTUAL COURSE (SPVC). ONCE YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. You will then have a 30 day window in which to complete your course. Within this 30 days, you will have 336 hours of elab time. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day, but lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Once you have accessed the course, help is available Monday through Friday; questions will be responded to within 24 hours.
NO EXTENSIONS FOR THIS COURSE WILL BE GRANTED.
This is the self paced training version of "Predictive Modeling with IBM SPSS Modeler" classroom course. This self paced training course demonstrates how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression, support vector machines, and Bayesian network models. Use of the binary classifier and numeric predictor nodes to automate model selection is included. Feature selection and detection of outliers are discussed. Expert options for each modeling node are reviewed in detail and advice is provided on when and how to use each model. You will also learn how to combine two or more models to improve prediction.
Målgrupp
This course follows either "Introduction to IBM SPSS Modeler and Data Mining" or Advanced Data Preparation with IBM SPSS Modeler is essential for anyone who wishes to become familiar with the full range of modeling techniques available in IBM SPSS Modeler to create predictive models.
Förkunskaper
General computer literacy. Experience using IBM SPSS Modeler (formerly Clementine) , including familiarity with the IBM SPSS Modeler environment, creating streams, reading in data files, assessing data quality and handling missing data (including the type and data audit nodes), basic data manipulation (including the derive and select nodes), and creation of models. Prior completion of Introduction to IBM SPSS Modeler and Data Mining is required and completion of Advanced Data Preparation with IBM SPSS Modeler is strongly encouraged. An introductory course in statistics, or equivalent experience, would be helpful for the statistics-based modeling techniques.
Nyckelområden
- Preparing data for modeling
- Searching for data anomalies
- Selecting predictors
- Data reduction with principal components
- Neural networks
- Support vector machines
- Cox regression
- Time series analysis
- Decision trees
- Linear regression
- Logistic regression
- Discriminant analysis
- Bayesian networks
- Numeric Predictor node
- Binary Classifier Node
- Combining models to improve performance
- Getting the most from models
- Appendix A: Decision List
Hårdvarukrav
Minimum Requirements (for PC) to use SPVC
- Windows 2000, XP, Vista, and 7
- Firefox, including V3.6.6
- Internet Explorer, including V8
- browser settings include:
- Java Runtime Environment must be installed and enabled in the web browser
- Web browser must be set to enable/allow JavaScript
- Web browser Pop-up blocking mechanisms must be set to allow pop-ups from elearn.ihost.com
- Cookies must be enabled
- Flash-enabled
- ActiveX-enabled
- CITRIX, for the remote lab (instructions to install included with the SPVC)