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 model2:  Setting measurement levels    •  Explain the concept of 'field measurement level'    •  Explain the consequences of incorrect measurement levels    •  Modify a field's 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

• Business Analysts• Data Scientists• Participants who want to get started with data science

• It is recommended that you have an understanding of your business data

Please refer to course overview