Getting Started with SAS - Your Gateway to Data Analytics

Welcome to the first part of our comprehensive SAS programming tutorial series! Whether you’re a complete beginner or someone looking to add SAS to your data analytics toolkit, this guide will help you take your first steps into the world of SAS programming.

What You’ll Learn

In this tutorial, you will:

• Understand what SAS is and why it’s valuable in today’s data-driven world
• Learn about career opportunities and industry demand for SAS skills
• Get hands-on experience setting up SAS for free
• Write and run your first SAS program
• Understand the basic structure of SAS programs
• Learn how to read and interpret SAS logs

Prerequisites

None! This tutorial is designed for complete beginners. All you need is:

• A computer with internet access
• Basic computer skills (using a web browser, creating accounts)
• Enthusiasm to learn a powerful data analytics tool

Why Learn SAS?

Industry Demand and Career Opportunities

SAS (Statistical Analysis System) remains one of the most sought-after skills in data analytics, particularly in:

• Healthcare and Pharmaceuticals: Clinical trials, drug safety analysis, epidemiological studies
• Banking and Finance: Risk management, fraud detection, regulatory compliance
• Government: Census data analysis, economic forecasting, policy evaluation
• Insurance: Actuarial analysis, claims processing, customer segmentation
• Retail: Customer analytics, inventory optimization, market basket analysis

The Numbers Speak for Themselves

According to recent job market data:

• Average SAS Programmer salary in the US: $75,000 - $120,000
• Over 70,000 job postings mention SAS skills annually
• 90% of Fortune 500 companies use SAS
• SAS certified professionals earn 15-25% more than non-certified peers

SAS vs. Other Statistical Software

Let’s compare SAS with other popular data analysis tools:

Feature	SAS	R	Python	SPSS
Industry Adoption	Very High (Enterprise)	High (Academia/Tech)	Very High (Tech)	Moderate
Learning Curve	Moderate	Steep	Moderate	Easy
Cost	Expensive (Free options available)	Free	Free	Expensive
Support	Professional	Community	Community	Professional
Data Handling	Excellent for large data	Good	Good	Limited
Statistical Procedures	Comprehensive	Comprehensive	Growing	Good
Regulatory Compliance	FDA approved	Limited	Limited	Limited

Why SAS Still Matters in 2025

1. Reliability: SAS has been tested and trusted for over 40 years
2. Comprehensive Documentation: Every procedure is thoroughly documented
3. Audit Trail: Built-in features for regulatory compliance
4. Enterprise Support: Professional support for mission-critical applications
5. Backward Compatibility: Code written decades ago still runs today

How to Access SAS

The great news is that you can start learning SAS for free! Here are your options:

1. SAS OnDemand for Academics (Recommended for Beginners)

This is the easiest way to get started with SAS - completely free and runs in your web browser.

Steps to Get Started:

1. Visit the SAS OnDemand Website
   • Go to https://welcome.oda.sas.com
   • Click on “Register for an account”
2. Create Your Profile
   • Fill in your personal information
   • Use a valid email address (you’ll need to verify it)
   • Choose “Independent Learner” if you’re not affiliated with an institution
3. Verify Your Email
   • Check your inbox for a verification email from SAS
   • Click the verification link
4. Complete Registration
   • Set up your password (make it strong!)
   • Accept the terms and conditions
   • Complete the registration process
5. Access SAS Studio
   • Log in with your credentials
   • You’ll see the SAS Studio interface load in your browser

2. SAS University Edition (Being Phased Out)

Note: SAS University Edition is being discontinued. New users should use SAS OnDemand instead.

3. Enterprise/Workplace Installations

If your organization has SAS:

• Contact your IT department for access
• You might have SAS installed locally or access via Citrix/Remote Desktop
• Enterprise versions often include additional modules

4. Cloud Options - SAS Viya

For advanced users and organizations:

• Cloud-native analytics platform
• Supports both SAS and open-source languages
• Requires enterprise licensing

First Look at SAS Interface

Once you’ve logged into SAS OnDemand, you’ll see SAS Studio - a modern, web-based interface for SAS programming.

Understanding the SAS Studio Workspace

The interface consists of several key areas:

1. Navigation Pane (Left Side)
   • Server Files and Folders: Your file system
   • Tasks and Utilities: Point-and-click tools
   • Snippets: Code templates
   • Libraries: Data storage locations
2. Work Area (Center)
   • Code Editor: Where you write SAS programs
   • Multiple tabs: Work on several programs simultaneously
   • Syntax highlighting: Different colors for different code elements
3. Results/Output Area
   • Results: Shows output from procedures
   • Log: Displays messages about program execution
   • Output Data: View created datasets
4. Toolbar
   • Run button: Execute your code
   • Save: Save your programs
   • Options: Customize your environment

Basic Navigation Tips

Let me show you some essential navigation commands:

/* This is a SAS comment - it won't execute but helps document your code */

* This is another way to write a comment in SAS;

/* To run code in SAS Studio:
   1. Type or paste your code in the editor
   2. Highlight the code you want to run (or run all)
   3. Click the "Run" button or press F3
*/

Your First SAS Program

Let’s write the traditional “Hello World” program in SAS!

Example 1: Basic Output

/* My First SAS Program
   Author: Your Name
   Date: Today's Date
   Purpose: Display a message to the log
*/

data _null_;
    put "Hello, World! Welcome to SAS Programming!";
run;

Detailed Explanation:

1. data _null_;
   • data - This keyword starts a DATA step (we’ll learn more about this later)
   • _null_ - A special dataset name that means “don’t create a dataset”
   • ; - Every SAS statement ends with a semicolon (VERY IMPORTANT!)
2. put "Hello, World! Welcome to SAS Programming!";
   • put - Command to write text to the log
   • The text in quotes is what will be displayed
   • Again, note the semicolon at the end
3. run;
   • run - Tells SAS to execute the preceding DATA step
   • Without RUN, SAS waits for more statements

Example 2: Creating Your First Dataset

Now let’s create actual data:

/* Creating a simple dataset with employee information */

data employees;
    input name $ age salary;
    datalines;
John 35 55000
Sarah 28 48000
Mike 42 72000
Lisa 31 58000
;
run;

/* Display the data we just created */
proc print data=employees;
    title "My First Employee Dataset";
run;

Detailed Explanation:

1. data employees;
   • Creates a dataset named “employees”
   • This dataset will be stored in the WORK library (temporary storage)
2. input name $ age salary;
   • input - Tells SAS what variables to expect
   • name $ - A character variable (the $ indicates character/text)
   • age - A numeric variable
   • salary - Another numeric variable
3. datalines;
   • Indicates that data values follow
   • Alternative keyword: cards; (they’re synonymous)
4. The actual data
   • Each line represents one observation (row)
   • Values are separated by spaces
   • The order matches the INPUT statement
5. ;
   • A semicolon on its own line ends the data input
6. proc print data=employees;
   • proc - Starts a PROCEDURE step
   • print - The procedure name (displays data)
   • data=employees - Specifies which dataset to print
7. title "My First Employee Dataset";
   • Adds a title to the output
   • Titles appear at the top of each page of output

Example 3: Simple Calculations

Let’s do some basic calculations:

/* Calculate annual bonus as 10% of salary */

data employees_with_bonus;
    set employees;  /* Read from existing dataset */
    bonus = salary * 0.10;
    total_compensation = salary + bonus;
    
    /* Add a label to make output clearer */
    label salary = "Annual Salary"
          bonus = "Annual Bonus (10%)"
          total_compensation = "Total Annual Compensation";
run;

/* Display the results with formatting */
proc print data=employees_with_bonus label;
    title "Employee Compensation Report";
    format salary bonus total_compensation dollar12.2;
run;

Detailed Explanation:

1. set employees;
   • Reads data from the existing “employees” dataset
   • Each observation is processed one at a time
2. bonus = salary * 0.10;
   • Creates a new variable called “bonus”
   • Calculates as 10% of salary
   • SAS uses standard math operators: + - * /
3. format salary bonus total_compensation dollar12.2;
   • format - Controls how values are displayed
   • dollar12.2 - Display as currency with 12 total characters, 2 decimal places
   • Example: $55,000.00

Understanding the SAS Log

The log is your best friend when programming in SAS. It tells you:

• What SAS did with your code
• Any errors or warnings
• Performance information

Reading Log Messages

Let’s intentionally create some errors to understand log messages:

/* Example with an error - missing semicolon */
data test
    input x y;
    datalines;
1 2
3 4
;
run;

Log Output:

ERROR: No DATALINES, INFILE, MERGE, SET, UPDATE or MODIFY statement.
ERROR 180-322: Statement is not valid or it is used out of proper order.

What went wrong? Missing semicolon after data test

Types of Log Messages

1. NOTES (Blue text)
   • Informational messages
   • Example: “NOTE: The data set WORK.TEST has 2 observations and 2 variables.”
2. WARNINGS (Green text)
   • Something unusual but not fatal
   • Example: “WARNING: Variable X already exists on file WORK.TEST.”
3. ERRORS (Red text)
   • Something is wrong and needs fixing
   • Example: “ERROR: Variable X not found.”

Best Practices for Reading Logs

1. Always check the log after running code
2. Start from the first error - Later errors might be cascading effects
3. Look for the line number - SAS tells you where the error occurred
4. Read the full error message - It often suggests solutions

Common Beginner Mistakes and How to Avoid Them

1. Forgetting Semicolons

Wrong:

data mydata
    input x y
run

Right:

data mydata;
    input x y;
run;

2. Incorrect Variable Types

Wrong:

data test;
    input name age;  /* name should be character */
    datalines;
John 25
;
run;

Right:

data test;
    input name $ age;  /* $ indicates character variable */
    datalines;
John 25
;
run;

3. Missing RUN Statements

Wrong:

proc print data=mydata;
/* Forgot run; - procedure won't execute */

proc means data=mydata;
run;

Right:

proc print data=mydata;
run;

proc means data=mydata;
run;

4. Case Sensitivity Confusion

Remember:

• SAS keywords are NOT case-sensitive: DATA, data, Data all work
• Variable values ARE case-sensitive: “John” ≠ “JOHN”
• Variable names are NOT case-sensitive: Name, name, NAME refer to the same variable

Practical Exercises

Exercise 1: Create a Student Dataset

Create a dataset with student information including name, age, and test score:

/* Your task: Create a dataset called 'students' with at least 5 students */
/* Include: name (character), age (numeric), test_score (numeric) */
/* Then display the data */

/* Write your code here */

Solution:

data students;
    input name $ age test_score;
    datalines;
Emma 20 95
Liam 19 87
Olivia 21 92
Noah 20 88
Ava 19 96
;
run;

proc print data=students;
    title "Student Test Scores";
run;

Exercise 2: Calculate Grade Categories

Extend the student dataset to include grade categories:

/* Add a grade category based on test scores:
   90-100: A
   80-89: B
   70-79: C
   Below 70: D
*/

/* Write your code here */

Solution:

data students_graded;
    set students;
    
    if test_score >= 90 then grade = 'A';
    else if test_score >= 80 then grade = 'B';
    else if test_score >= 70 then grade = 'C';
    else grade = 'D';
run;

proc print data=students_graded;
    title "Student Grades Report";
run;

Exercise 3: Basic Statistics

Calculate the average test score:

/* Use PROC MEANS to find the average test score */
/* Write your code here */

Solution:

proc means data=students;
    var test_score;
    title "Test Score Statistics";
run;

Tips for Success

1. Practice Regularly

• Try to code a little bit every day
• Start with simple programs and gradually increase complexity

2. Use Comments Liberally

/* Good commenting practice */
data analysis;
    /* Calculate customer lifetime value */
    set customers;
    
    /* Average purchase × frequency × retention period */
    lifetime_value = avg_purchase * purchase_freq * retention_years;
run;

3. Organize Your Code

/******************************************
* Program: customer_analysis.sas          *
* Author: Your Name                       *
* Date: 2025-08-09                       *
* Purpose: Analyze customer purchase data *
******************************************/

/* Step 1: Import data */
/* Step 2: Clean and prepare */
/* Step 3: Analysis */
/* Step 4: Report results */

4. Save Your Work Frequently

• SAS Studio auto-saves, but manual saves are still important
• Use meaningful file names: project1_data_cleaning.sas

5. Build a Code Library

• Save useful code snippets
• Create templates for common tasks
• Document what each snippet does

Summary and Key Takeaways

Congratulations! You’ve taken your first steps into SAS programming. Here’s what we covered:

1. Why SAS Matters
   • Industry standard in healthcare, finance, and government
   • Excellent career opportunities
   • Robust and reliable for enterprise use
2. Getting Started
   • SAS OnDemand for Academics - free and browser-based
   • SAS Studio interface - modern and user-friendly
3. Basic Programming Concepts
   • DATA steps create and manipulate data
   • PROC steps analyze and report on data
   • Every statement ends with a semicolon
   • The log is essential for debugging
4. Your First Programs
   • Created datasets using INPUT and DATALINES
   • Performed calculations and transformations
   • Displayed results with PROC PRINT

What’s Next?

In Part 2 of our series, “SAS Fundamentals - Building Your Foundation,” we’ll dive deeper into:

• Understanding DATA steps vs. PROC steps
• Working with libraries and permanent datasets
• SAS naming conventions and rules
• More complex data operations
• Introduction to SAS functions

Additional Resources

1. SAS Documentation: support.sas.com
2. SAS Communities: communities.sas.com
3. Practice Datasets: Available in SAS OnDemand’s SASHELP library

Practice Dataset for Download

Save this as first_practice.csv:

employee_id,first_name,last_name,department,hire_date,salary
1001,John,Smith,Sales,01JAN2020,55000
1002,Sarah,Johnson,Marketing,15MAR2019,48000
1003,Mike,Williams,IT,22JUN2018,72000
1004,Lisa,Brown,HR,08SEP2021,58000
1005,David,Jones,Finance,30NOV2017,65000

Try importing this file using:

proc import datafile='path/to/first_practice.csv'
    out=practice_data
    dbms=csv
    replace;
run;

Happy SAS programming! Remember, every expert was once a beginner. Keep practicing, stay curious, and don’t be afraid to make mistakes - they’re the best teachers!