SAS Enterprise Guide - Query, Analytics and Reporting

SAS Enterprise Guide - Query, Analytics and Reporting

SAS Enterprise Guide is a point-and-click, menu- and wizard-driven tool that empowers users to analyze data and publish their results. It provides fast-track learning for quick data investigations, generating the code for greater productivity, accelerating deployment of analyses and forecasts. This course equips the learner with the skills necessary to operate the tool. This is a HIGH INTENSITY course.

Learning Outcome

• Machine Learning with SAS
• Perform analysis and statistics with a self-service environment for workflow-based projects.
• Make reporting and analytics with flexible distribution mechanisms.
• Utilize intuitive, flexible wizards to access SAS capabilities from reporting to complex analyses.
• Perform reporting, graphical and analytical tasks.
• Execute data access and management.
• Use subqueries to include results from one query as input to another query.
• Parameterize data to interactively filter information.
• Organize information for easy viewing with a Select and Sort interface. Format
• existing variables and create new calculated variables.
• Visually build calculated variables from a comprehensive list of SAS functions.
• Create graphs including area charts, bar charts, box plots, bubble plots, donut charts, line plots, maps, pie charts, radar charts, scatter plots, surface plots and contour plots.

Prerequisites

This is not a beginners’ course. The learner must have the following skills prior to starting this curriculum:

• Ability to code in SAS
• Ability to integrate data to and from SAS
• Perform analytics in SAS

If you have never used SAS or are completely inexperienced to SAS, it is recommended that you acquaint yourself with a beginners’ course on SAS prior to venturing into this course.

Outline

Duration: 2 days

Duration: 8 hours per day (inclusive of an hour’s lunch break and two short 15 minute breaks)

Intensity: High

Difficulty: Medium

Mode of evaluation: Practical assessment

1. Overview
   1. UI
   2. Workspace
   3. Data Source
   4. Descriptive analyses
   5. Predictive analyses
2. Data
   1. Types
   2. Psychographic data
   3. Category comparison
   4. Sources
3. Analytics and Machine Learning
   1. Frequency distributions
   2. Cluster
   3. Decision Tree
   4. Linear regression
   5. Logistic regression
   6. Neural networks
4. Modeling
   1. Modeling Process
   2. Definitions
   3. Development of the model
   4. Implementation
5. Data Construction
   1. Prospect models
   2. Customer models
   3. Risk models
6. Case
   1. Project initiation
   2. Exploratory analyses
   3. Segmentation and profile analyses
   4. Correlation analyses
7. Conclusion
   1. Assessment
   2. Evaluation