Data science without a Ph.D. Using IBM SPSS Modeler v18.1.1 0A018G

• Introduction to data science and IBM SPSS Modeler
• Setting measurement levels
• Exploring the data
• Using automated data preparation
• Partitioning the data
• Selecting predictors
• Using automated modeling
• Evaluating models
• Deploying models

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

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