SkyLimit Tech Hub: Data Science Training Center

Course 1: Introduction to SAS Programming Environment

Welcome to the SAS Foundations Certificate Program! This course provides a comprehensive introduction to the SAS (Statistical Analysis System) programming environment. You will learn about the SAS ecosystem, navigate various SAS interfaces, manage SAS libraries and data sets, read and write data, and write basic SAS programs. Designed for beginners, this course equips you with the foundational skills needed for data management and analysis in SAS over one week.

Objective: By the end of the course, learners will be proficient in navigating SAS interfaces, managing data in SAS libraries, importing/exporting data, and writing basic SAS programs using DATA and PROC steps for effective data analysis.

Scope: The course covers the SAS ecosystem, SAS Studio and Enterprise Guide interfaces, SAS libraries and data sets, data import/export, and basic SAS syntax, using practical examples and hands-on exercises.

Day 1: Overview of SAS and Its Ecosystem

Introduction: This session provides a broad overview of SAS (Statistical Analysis System) and its ecosystem. You'll learn about the history of SAS, its various products and solutions, and the diverse applications of SAS in different industries.

Learning Objective: By the end of this session, you will be able to describe the history and evolution of SAS, identify the key components of the SAS ecosystem, and explain the diverse applications of SAS in various industries.

Scope of the Lesson: This lesson covers the history of SAS, key components of the SAS ecosystem (including Base SAS, SAS/STAT, SAS/GRAPH, SAS Enterprise Guide, SAS Studio, and SAS Visual Analytics), and applications of SAS in industries such as healthcare, finance, retail, and government.

Background Information: SAS began in the 1960s as a project to analyze agricultural data. It has since evolved into a comprehensive suite of software solutions used worldwide for data management, advanced analytics, business intelligence, and more.

Hands-On Example:

* Example 1: Displaying SAS version information;
PROC PRODUCT_STATUS;
RUN;

* Example 2: Exploring available SAS procedures;
PROC CONTENTS DATA=_ALL_ NODS;
RUN;

* Example 3: Accessing SAS help documentation;
PROC HELP;
RUN;

* Example 4: Displaying SAS system options;
PROC OPTIONS;
RUN;

* Example 5: Checking SAS environment variables;
PROC SETINIT;
RUN;

Interpretation: Example 1 displays SAS version information using PROC PRODUCT_STATUS. Example 2 explores available SAS procedures using PROC CONTENTS. Example 3 accesses SAS help documentation using PROC HELP. Example 4 displays SAS system options using PROC OPTIONS. Example 5 checks SAS environment variables using PROC SETINIT.

Supplemental Information:

Discussion Points:

  • What are the key milestones in the history of SAS?
  • What are the main components of the SAS ecosystem, and what are their primary functions?
  • In what industries is SAS commonly used, and what types of problems does it solve?
  • How has SAS evolved to meet the changing needs of data analysis?
  • What are the advantages of using SAS compared to other statistical software?

Day 2: Navigating the SAS Interface

Introduction: This session explores the various interfaces available for working with SAS, including SAS Studio, SAS Enterprise Guide, and the command-line interface.

Learning Objective: By the end of this session, you will be able to navigate and utilize SAS Studio, SAS Enterprise Guide, and the command-line interface, understanding their strengths and weaknesses.

Scope of the Lesson: This lesson covers SAS Studio (code editor, log window), SAS Enterprise Guide (project-based approach, process flows), and the command-line interface (submitting code, batch processing).

Background Information: SAS offers multiple interfaces to cater to different user needs. SAS Studio is web-based, SAS Enterprise Guide is project-based, and the command-line interface offers flexibility for advanced users.

Hands-On Example:

* Example 1: Running a simple SAS program in SAS Studio;
PROC PRINT DATA=sashelp.class;
RUN;

* Example 2: Creating a project in SAS Enterprise Guide;
/* Steps to create a project, add data, and run a task */

* Example 3: Submitting a SAS program from the command line;
/* sas myprogram.sas */

* Example 4: Viewing the log in SAS Studio;
/* Examine the log window for errors and warnings */

* Example 5: Using the Enhanced Editor in SAS Studio;
/* Use code completion, syntax highlighting, and other features */

Interpretation: Example 1 runs a SAS program in SAS Studio. Example 2 outlines creating a project in SAS Enterprise Guide. Example 3 submits a program from the command line. Example 4 explains viewing the log in SAS Studio. Example 5 highlights the Enhanced Editor features in SAS Studio.

Supplemental Information:

Discussion Points:

  • What are the key differences between SAS Studio, SAS Enterprise Guide, and the command-line interface?
  • What are the advantages and disadvantages of each SAS interface?
  • For what types of tasks is each SAS interface best suited?
  • How can you customize the SAS interface to improve productivity?
  • What are tips for navigating the SAS interface efficiently?

Day 3: Understanding SAS Libraries and Data Sets

Introduction: This session explores SAS libraries and data sets, fundamental concepts for managing and organizing data in SAS.

Learning Objective: By the end of this session, you will be able to create and access SAS libraries, understand their types, and describe the structure and properties of SAS data sets.

Scope of the Lesson: This lesson covers creating and accessing SAS libraries (temporary and permanent), understanding SASHELP and WORK libraries, describing SAS data set structure (variables, observations, metadata), and managing libraries and data sets.

Background Information: SAS libraries are containers for SAS data sets. SAS data sets are rectangular files storing data in a structured format.

Hands-On Example:

* Example 1: Creating a temporary SAS library;
LIBNAME mylib 'C:\SASdata';

* Example 2: Creating a permanent SAS library;
LIBNAME mylib 'C:\SASdata';

* Example 3: Accessing a SAS data set;
DATA mylib.newdata;
  SET sashelp.class;
RUN;

* Example 4: Listing the contents of a SAS library;
PROC CONTENTS DATA=mylib._ALL_ NODS;
RUN;

* Example 5: Deleting a SAS data set;
PROC DATASETS LIBRARY=mylib;
  DELETE newdata;
RUN;

Interpretation: Example 1 creates a temporary SAS library. Example 2 creates a permanent SAS library. Example 3 accesses a data set. Example 4 lists library contents. Example 5 deletes a data set.

Supplemental Information:

Discussion Points:

  • What are the differences between temporary and permanent SAS libraries?
  • What are the purposes of SASHELP and WORK libraries?
  • How can you determine the structure of a SAS data set?
  • What are best practices for managing SAS libraries and data sets?
  • How can SAS libraries organize and protect data?

Day 4: Reading and Writing Data in SAS

Introduction: This session focuses on reading data into SAS and writing data out of SAS, covering CSV, Excel, and TXT files.

Learning Objective: By the end of this session, you will be able to import data from CSV, Excel, and TXT files into SAS data sets and export data from SAS data sets to these formats.

Scope of the Lesson: This lesson covers importing data using PROC IMPORT and DATA step, exporting data using PROC EXPORT and DATA step, and using LIBNAME for Excel files.

Background Information: SAS provides methods like PROC IMPORT, PROC EXPORT, and DATA step with INFILE/FILE statements for reading and writing data.

Hands-On Example:

* Example 1: Importing data from a CSV file using PROC IMPORT;
PROC IMPORT DATAFILE="C:\data\mydata.csv"
  OUT=work.imported_data
  DBMS=CSV
  REPLACE;
RUN;

* Example 2: Importing data from an Excel file using PROC IMPORT;
PROC IMPORT DATAFILE="C:\data\mydata.xlsx"
  OUT=work.imported_data
  DBMS=EXCEL
  REPLACE;
RUN;

* Example 3: Importing data from a TXT file using the DATA step;
DATA work.text_data;
  INFILE "C:\data\mydata.txt" DELIMITER=',' DSD;
  INPUT var1 var2 var3;
RUN;

* Example 4: Exporting data to a CSV file using PROC EXPORT;
PROC EXPORT DATA=work.imported_data
  OUTFILE="C:\data\exported_data.csv"
  DBMS=CSV
  REPLACE;
RUN;

* Example 5: Exporting data to a TXT file using the DATA step;
DATA _NULL_;
  SET work.imported_data;
  FILE "C:\data\exported_data.txt" DELIMITER=',' DSD;
  PUT var1 var2 var3;
RUN;

Interpretation: Example 1 imports a CSV file. Example 2 imports an Excel file. Example 3 imports a TXT file. Example 4 exports to a CSV file. Example 5 exports to a TXT file.

Supplemental Information:

Discussion Points:

  • What are the advantages of PROC IMPORT vs. DATA step?
  • How can you handle delimiters and missing values in TXT files?
  • How can LIBNAME access Excel files?
  • What are best practices for exporting data?
  • How can you handle large data files efficiently?

Day 5: Basic SAS Syntax and Program Structure

Introduction: This session covers the fundamental syntax and structure of SAS programs, focusing on DATA and PROC steps.

Learning Objective: By the end of this session, you will be able to write basic SAS programs using DATA and PROC steps, understand SAS syntax rules, and identify/correct common syntax errors.

Scope of the Lesson: This lesson covers DATA step (reading data, creating variables), PROC step (data analysis, reporting), SAS syntax rules, and common syntax errors.

Background Information: SAS programs consist of DATA steps for data manipulation and PROC steps for analysis and reporting.

Hands-On Example:

* Example 1: Creating a SAS data set using the DATA step;
DATA work.new_data;
  INPUT var1 var2 var3;
  DATALINES;
  1 2 3
  4 5 6
  ;
RUN;

* Example 2: Printing a SAS data set using the PROC PRINT procedure;
PROC PRINT DATA=work.new_data;
RUN;

* Example 3: Calculating descriptive statistics using the PROC MEANS procedure;
PROC MEANS DATA=work.new_data;
  VAR var1 var2 var3;
RUN;

* Example 4: Using options in a PROC step;
PROC PRINT DATA=work.new_data NOOBS;
RUN;

* Example 5: Using conditional logic in a DATA step;
DATA work.conditional_data;
  SET work.new_data;
  IF var1 > 2 THEN var4 = 1;
  ELSE var4 = 0;
RUN;

Interpretation: Example 1 creates a data set. Example 2 prints a data set. Example 3 calculates statistics. Example 4 uses options in PROC PRINT. Example 5 uses conditional logic.

Supplemental Information:

Discussion Points:

  • What are the differences between DATA and PROC steps?
  • What are the essential components of a DATA step?
  • What are common SAS procedures and their tasks?
  • What are the rules for naming variables and data sets?
  • How can you identify and correct syntax errors?

Daily Quiz

Practice Lab

Select an environment to practice coding exercises. Use SAS OnDemand for Academics for a free SAS programming environment.

Exercise

Download the following files to support your learning:

Grade

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