Eikosstatistics & data science

Programming & Scientific Computing · Statistical & BI Software

SAS: Data Management and Preparation

Public introduction · full guide in Eikos

Introduction

What this guide covers

Rigorous and exhaustive treatment of the SAS environment as a tool for management, transformation, integration and validation of statistical data. It covers everything from the architecture of the PDV engine to advanced programming techniques using hash tables and macros.

Overview

Topics covered

  • Introduction
  • SAS Environment Architecture
  • 1.1 The SAS Dataset as a Fundamental Unit
  • 1.2 SAS Libraries (Librefs)
  • 1.3 Execution Flow in SAS
  • 1.4 The Program Data Vector (PDV)

Related concepts

Main connections

  • SQL & relational data modeling
  • Analysis of variance (ANOVA)
  • Relational joins & entity integrity
  • Missing data & multiple imputation
  • Supervised machine learning
  • Preprocessing pipelines & transform recipes

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

Cicloi:𝙸𝙽𝙸𝚃(PDV)→𝚁𝙴𝙰𝙳(ri)→𝙴𝚇𝙴𝙲𝚄𝚃𝙴(sentencias)→𝚆𝚁𝙸𝚃𝙴(ri' a output)
View LaTeX source\text{Ciclo}_i : \; \texttt{INIT}(\text{PDV}) \;\to\; \texttt{READ}(r_i) \;\to\; \texttt{EXECUTE}(\text{sentencias}) \;\to\; \texttt{WRITE}(r_i' \text{ a output})