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Course Outline

Overview

Comprehensive Review of SAS

  • Structure of SAS data sets
  • Definition and management of SAS variables
  • Organization within SAS libraries
  • Standardized structure of SAS code

Establishment of the Development Environment

  • Installation and configuration of SAS Studio
  • Installation and configuration of WPS

Data Management Practices in SAS

  • Ingestion of external data sources
  • Export of processed data
  • Development of variables and computational logic
  • Selection and filtering of observations
  • Implementation of conditional logic and iterative processes
  • Integration of multiple data sets
  • Application of core SAS statements
  • Data cleansing procedures

Advanced Arrays and Functional Methods

  • Generation of new variables through iterative loops
  • Construction of derived variables
  • Utilization of standard SAS functions
  • Consolidation of raw data files for government reporting

Data Visualization Techniques

  • Development of bar charts
  • Development of scatter plots
  • Development of pie charts
  • Layering of graphical overlays

Statistical Analysis

  • Compilation of statistical reports
  • Application of simple linear regression models
  • Application of multiple regression analyses
  • Interpretation of analytical results
  • Forecasting and predictive capabilities

Structured Query Language (SQL) in SAS

  • Fundamentals of SAS SQL syntax
  • Implementation of clauses and statements
  • Manipulation of columns and rows
  • Management of relational tables

Indexing Strategies in SAS

  • Validation with test data sets
  • Execution of PROC procedures for indexing
  • Creation, modification, and application of indexes to improve access for government systems

Macro Programming in SAS

  • Utilization of macro variables for dynamic content
  • Utilization of macro functions
  • Development of custom macros
  • Debugging techniques and storage management of macros

Predictive Modeling Frameworks

  • Application of linear regression techniques
  • Application of multiple regression techniques
  • Evaluation of underlying data patterns
  • Selection and preparation of input variables
  • Execution of PROC MI for missing data imputation

Summary and Conclusions

Requirements

  • Proficiency in managing hierarchical directory frameworks

Target Audience

  • Data Analysts
 14 Hours

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