Data Science without a Ph.D. Using IBM SPSS Modeler (v18.1.1) SPVC

1: Introduction to data science and IBM SPSS Modeler
• Explain the stages in a data-science project, using the CRISP-DM methodology
• Create IBM SPSS Modeler streams
• Build and apply a machine learning model
2: Setting measurement levels
• Explain the concept of "field measurement level"
• Explain the consequences of incorrect measurement levels
• Modify a field's measurement level
3: Exploring the data
• Audit the data
• Check for invalid values
• Take action for invalid values
• Impute missing values
• Replace outliers and extremes
4: Using automated data preparation
• Automatically exclude low quality fields
• Automatically replace missing values
• Automatically replace outliers and extremes
5: Partitioning the data
• Explain the rationale for partitioning the data
• Partition the data into a training set and testing set
6: Selecting predictors
• Automatically select important predictors (features) to predict a target
• Explain the limitations of automatically selecting features
7: Using automated modeling
• Find the best model for categorical targets
• Find the best model for continuous targets
• Explain what an ensemble model is
8: Evaluating models
• Evaluate models for categorical targets
• Evaluate models for continuous targets
9: Deploying models
• List two ways to deploy models
• Export scored data