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

Detailed Course Outline

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 fields 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