Outline detalhado do curso
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 model2: Setting measurement levels • Explain the concept of "field measurement level" • Explain the consequences of incorrect measurement levels • Modify a fields measurement level3: Exploring the data • Audit the data • Check for invalid values • Take action for invalid values • Impute missing values • Replace outliers and extremes4: Using automated data preparation • Automatically exclude low quality fields • Automatically replace missing values • Automatically replace outliers and extremes5: Partitioning the data • Explain the rationale for partitioning the data • Partition the data into a training set and testing set6: Selecting predictors • Automatically select important predictors (features) to predict a target • Explain the limitations of automatically selecting features7: Using automated modeling • Find the best model for categorical targets • Find the best model for continuous targets • Explain what an ensemble model is8: Evaluating models • Evaluate models for categorical targets • Evaluate models for continuous targets9: Deploying models • List two ways to deploy models • Export scored data