Advanced Analytics Professional

Learn how to:

• Define a SAS Enterprise Miner project and explore data graphically.
• Modify data for better analysis results.
• Build and understand predictive models, including decision trees and regression models.

• Compare and explain complex models.
• Generate and use score code.
• Apply association and sequence discovery to transaction data.

Learn how to:

• Construct multilayer perceptron and radial basis function neural networks.
• Construct custom neural networks using the NEURAL procedure.
• Choose an appropriate network architecture and determining the relevant training method.

• Avoid overfitting neural networks.

• Perform autoregressive time series analysis using neural networks.

• Interpret neural network models.

Learn how to:

• Use logistic regression to model an individual's behavior as a function of known inputs.
• Select variables and interactions.
• Create effect plots and odds ratio plots using ODS Statistical Graphics.
• Handle missing data values.

• Tackle multicollinearity in your predictors.

• Assess model performance and compare models.
  
• Recode categorical variables based on the smooth weight of evidence.
• Use efficiency techniques for massive data sets.

Learn how to:

• Use applications designed for big data analyses.
• Explore data efficiently.
• Reduce data dimensionality.
• Build predictive models using decision trees, regressions, generalized linear models, random forests and support vector machines.

• Build models that handle multiple targets.

• Assess model performance.
  
• Implement models and score new predictions.

Learn how to:

• Call R packages in SAS.
• Use Python scripts in SAS.
• Integrate open source data exploration techniques in SAS.

• Integrate open source models in SAS Enterprise Miner.

• Create production (score) code for R models.

Learn how to:

• Convert documents stored in standard formats (Microsoft Word, Adobe PDF, etc.) into general-purpose HTML or TXT formats.

• Read documents from a variety of sources (web pages, flat files, data elements in a relational database, spreadsheet cells, etc.) into SAS tables.

• Process textual data for text mining (e.g., correcting misspellings or recoding acronyms and abbreviations).
  
• Convert unstructured text-based character data into structured numeric data.
• Explore words and phrases in a document collection.
• Query document collections using keywords (i.e., identifying documents that include specific words or phrases).

• Identify topics or concepts that appear in a document collection.

• Create user-influenced topic tables from scratch or by modifying machine-generated topics, or creating concepts using domain knowledge.

• Use derived topic tables or preexisting user-influenced topic tables (or both) to enhance information retrieval and document classification.
• Cluster documents into homogeneous subgroups.
• Classify documents into predefined categories.

Learn how to:

• Create time series data.

• Accommodate trend, as well as seasonal and event-related variation, in time series models.

• Diagnose, fit and interpret exponential smoothing, ARIMAX and UCM models.
• Identify relative strengths and weaknesses of the three model types.

Learn how to:

• Define common terminology in designed experiments.

• Describe the benefits of multifactor experiments.

• Differentiate between the impact of a model and the impact of the action taken from that model.
• Fit incremental response models to evaluate the unique contribution of a marketing message, action, intervention or process change on outcomes.

Learn how to:

• Identify and formulate appropriate approaches to solving various linear and nonlinear optimization problems.

• Create optimization models commonly used in industry.

• Formulate and solve a data envelopment analysis.
• Solve optimization problems using the OPTMODEL procedure in SAS.