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 Duration 14 hours

Course Outline

Overview

Detailed Examination of SAS Capabilities

  • Structure and composition of SAS data sets
  • Definition and attributes of SAS variables
  • Organization and storage within SAS libraries
  • Syntax and architecture of SAS code

Configuration of the Development Environment

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

Data Administration in SAS

  • Data ingestion processes
  • Data exportation methods
  • Derivation of variables and computational logic
  • Selection and filtration of observations
  • Implementation of conditional logic and iterative processes
  • Integration of multiple data sets
  • Utilization of specific SAS statements
  • Data hygiene and quality assurance

Application of Arrays and Functions

  • Generation of new variables using iterative loops
  • Construction of derived variables
  • Utilization of native SAS functions
  • Consolidation of raw data files

Data Visualization Techniques

  • Generation of bar charts
  • Creation of scatter plots
  • Development of pie graphs
  • Superimposition of graphical elements

Statistical Analysis

  • Reporting and summarization of data
  • Application of linear regression models
  • Execution of multiple regression analyses
  • Interpretation of statistical findings
  • Development of predictive forecasts

SAS SQL Implementation

  • Syntax standards for SAS SQL
  • Application of SQL clauses and commands
  • Manipulation of columns and row structures
  • Management of tabular data structures

SAS Indexing Strategies

  • Performance testing with data sets
  • Utilization of PROC procedures
  • Creation, maintenance, and application of indexes

SAS Macro Programming

  • Utilization of macro variables
  • Application of macro functions
  • Development of macro routines
  • Troubleshooting and persistence of macro code

Predictive Modeling Frameworks

  • Implementation of linear regression models
  • Application of multiple regression models
  • Analysis of data patterns and trends
  • Integration of input variables
  • Utilization of PROC MI for missing data handling

Summary and Concluding Remarks

Requirements

  • Familiarity with navigating file and folder hierarchies

Target Audience

  • Data Analysts

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