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Sarah Rahbek

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Imposter Syndrome in the SAS World: Why You’re Better Than You Think

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Have you ever stared at a SAS log filled with errors and thought, “I have no idea what I’m doing”? Or compared yourself to another programmer and wondered how they make it all look so easy? If so, you’re not alone, and you’re definitely not a fraud. You might just be experiencing imposter syndrome, and it happens to even the most experienced SAS programmers.

Let’s talk about it.

What Is Imposter Syndrome?

Imposter syndrome is that nagging voice that tells you you’re not good enough, that your successes are flukes, and that it’s only a matter of time before someone finds out you don’t really know what you’re doing.

For SAS programmers, this might look like:

  • Feeling like a beginner, even after months or years of coding
  • Doubting your skills when facing a complex data step or macro
  • Avoiding asking questions because you’re afraid you should already know the answer
  • Believing everyone else “gets it” and you don’t

Spoiler: most of us have been there.

Why SAS Can Make You Feel This Way

SAS is a powerful and wide-reaching tool. It handles everything from basic data cleaning to advanced statistical modeling, predictive analytics, and clinical trial submissions. With such a vast range, it’s easy to feel like you’re always behind or that there’s so much more you “should” know.

Combine that with its unique syntax and procedural structure, and it’s no wonder some users feel overwhelmed, even seasoned analysts.

You’re Doing Better Than You Think

Here’s the truth: if you’re writing code, asking questions, and learning even a little bit every day, you are making progress.

Here’s what progress in the SAS world really looks like:

  • Remembering to check the log before panicking
  • Debugging a macro without deleting the whole program
  • Knowing when to use PROC FREQ instead of PROC MEANS
  • Finally understanding how BY statements work in merges
  • Writing comments in your code because future-you deserves kindness

None of that comes overnight. And guess what? Even SAS veterans Google things, copy from old programs, and curse at their log files.

You Are Not Alone

The SAS community is full of people who’ve felt the same way you do. Many of them are now teaching, mentoring, and speaking at conferences. Not because they were always confident, but because they pushed through their doubts.

Ask any SAS programmer about their first DATA step. Or the time they accidentally sorted a dataset without a BY statement and lost hours of work. Or their first PROC SQL that returned an empty table. You’ll hear the same mix of frustration, humility, and eventual pride.

Tips to Beat Imposter Syndrome as a SAS Programmer

  1. Keep a Win Log: Every time you solve a bug or finish a task, write it down. Celebrate your small victories.
  2. Ask Questions Without Shame: Every expert once had the same questions. There are no dumb questions, only missed opportunities to grow.
  3. Practice in a Safe Space: Use free environments like SAS OnDemand for Academics or test libraries to experiment freely.
  4. Connect with the Community: Join forums like SAS Communities, LinkedIn groups, or local user groups. You’re likely to find support and even mentorship.
  5. Remind Yourself: You Belong Here: If you’re learning, contributing, or just curious you belong in this space.

Final Thoughts

Feeling unsure doesn’t mean you’re an imposter, it means you’re growing. SAS is a journey, not a test. There will always be more to learn, and that’s actually the best part.

So, the next time that voice creeps in and whispers “You’re not good enough,” answer back with:
“Actually, I’m learning. And that makes me exactly where I’m supposed to be.”

Keep going. You’re better than you think.

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How to Get Started with SAS: A Beginner’s Guide to SAS Programming

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If you’re new to data analytics or just starting with SAS (Statistical Analysis System), welcome! You’re entering a powerful world of data manipulation, analysis, and visualization used across industries from healthcare to finance. In this beginner’s guide, we’ll walk through how to get started with SAS and the basics you need to know to hit the ground running.

What is SAS?

SAS is a software suite developed by SAS Institute for advanced analytics, business intelligence, data management, and predictive analytics. It’s particularly popular in regulated industries like pharmaceuticals, banking, and insurance due to its robustness and auditability.

Why use SAS?

  • Reliable for large data sets
  • Excellent reporting and statistical capabilities
  • Strong industry support (especially in healthcare and finance)
  • Built-in procedures for complex statistical methods

How to Access SAS

There are several ways to start using SAS:

  1. SAS OnDemand for Academics (Free)
    • Ideal for students and educators.
    • Cloud-based, no installation needed.
    • Sign up at SAS OnDemand for Academics.
  2. SAS University Edition (Deprecated)
    • Previously offered a free local install; now replaced by cloud options.
  3. SAS Viya
    • Modern, cloud-native analytics platform with an intuitive UI.
  4. Enterprise-Level Installation
    • Typically provided by employers with licensing agreements.

The SAS Programming Basics

SAS programs are built around two main steps:

1. DATA Step

Used for reading, modifying, and creating data sets. The structure of a basic data step where data is manually input is listed below.

2. PROC Step

Used to analyze or report on the data.

Key SAS Concepts

  • Libraries: Collections of datasets. WORK is the temporary default library.
  • Data Sets: Tables stored in SAS libraries.
  • Variables: Columns in a dataset.
  • Observations: Rows in a dataset.
  • Procedures (PROC): Built-in tools for analysis (e.g., PROC MEANS, PROC FREQ, PROC SORT).

Hands-On: Your First SAS Program

Try this simple program to explore your first dataset. This will display the mean, min, max, and other statistics for the Score variable.

Tips for SAS Beginners

  • Use comments liberally: * This is a comment; or /* Block comment */
  • Run your code in sections to avoid re-running everything.
  • Explore SAS documentation—it’s thorough and beginner-friendly.
  • Don’t fear the log—it tells you what happened, what didn’t, and why.

What’s Next?

Once you’re comfortable with basic data steps and procedures, try exploring:

  • PROC SORT and PROC FREQ
  • Creating graphs with PROC SGPLOT
  • Using conditional logic with IF/THEN statements
  • SAS Macros for automation

Final Thoughts

SAS might seem intimidating at first, but with practice, it becomes a powerful ally in your data journey. Whether you’re analyzing survey data, creating dashboards, or building predictive models, SAS provides the tools to get the job done.

Stay tuned for more beginner-friendly tutorials and happy coding!

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How to Comment Your Code for Future You (and Your Team)

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As data analysts, we often focus on solving the problem in front of us—cleaning the data, building a model, or producing that final report. But have you ever opened your old SAS code and had no idea what you were thinking? You’re not alone. Documenting your code is one of the most undervalued (yet critical) habits for long-term productivity and team collaboration.

Let’s walk through practical ways to document your SAS code effectively—so that 3-months-from-now you (or your teammates) can pick it up with zero confusion.

Why Bother with Documentation?

  • You’ll save hours when revisiting old projects.
  • Team members can understand, reuse, and build on your work.
  • You reduce errors and rework caused by ambiguous code.
  • It’s a huge win when onboarding new analysts or handing off work.

How to Comment Your Code in SAS: Best Practices + Examples

1. Use Clear Section Headers

Break your code into logical sections with comments as signposts.

2. Comment Why, Not Just What

It’s tempting to say what the code is doing—but what’s more useful is why you’re doing it. This gives your future self (and team members) the context behind the decision.

3. Describe Key Variables

When working with datasets, document important variables and any transformations you make.

Even better, include a quick variable dictionary at the top of your code or in a separate README:

4. Explain Complex Logic or Business Rules

If your analysis includes tricky conditions or business-specific logic, add inline comments.

This saves your team from guessing what “>2” means or why the 6-month window was chosen.

5. Create a Summary at the Top

For longer projects, start your program with a short summary block. This helps you or someone else understand the purpose of the script without reading every line.

6. Use Meaningful Names for Datasets and Variables

Avoid generic names like data1 or temp2. Make your names descriptive.

Develop Your Own Commenting Style

As you get more comfortable documenting your SAS code, you’ll naturally develop your own commenting style—and that’s a good thing. Maybe you like writing short summaries before each data step, or perhaps you prefer inline comments next to critical lines of logic.

What matters most is consistency and clarity—both for yourself and anyone else reading your code. Over time, your comment style becomes part of your analytical voice, and it can make your work more approachable and professional.

Final Thoughts

Writing clean, well-documented SAS code isn’t just polite—it’s professional. It makes your work reusable, understandable, and trustworthy. Future you (and your team) will thank you.

Start small: add a project header, explain one business rule, or describe a derived variable. Over time, good documentation becomes second nature—and a massive asset to your career.

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How to Import a TXT File into SAS (Step-by-Step Guide for Beginners)

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If you’re working with data in SAS, chances are you’ll need to import a TXT file at some point. Whether it’s survey responses, transaction logs, or exported reports, plain text files are a common format for raw data.

This guide will show you how to import a TXT file into SAS using both the DATA step and PROC IMPORT methods – perfect for beginners who want clean, organized data inside SAS.

Before You Start: Know Your File Format

TXT files come in different shapes and sizes. The two most common formats are:

  • Delimited (e.g., tab-separated, comma-separated)
  • Fixed-width (each field has a fixed number of characters)

Check your TXT file in a text editor like Notepad or VS Code to understand its structure. That will determine which method you use.

What Is a Delimiter?

A delimiter is a character or symbol used to separate individual pieces of data in a text file—especially in plain text formats like .txt or .csv.

Think of a delimiter as the “separator” between data values. It helps programs like SAS know where one value ends and the next one begins.

Using PROC IMPORT

Let’s use data from the TXT file below as an example for this tutorial. The data has imaginary names and test scores which are separated by a space.

To import this TXT file, we could use the PROC IMPORT code below.

What This Does:

  • OUT= names your new SAS dataset
  • DATAFILE= points to your TXT file
  • DBMS=dlm tells SAS it’s a delimited file
  • DELIMITER=dlm sets space as the delimiter

Since our test data was neatly formatted with single-space delimiters, the data was successfully imported and resulted in the SAS dataset below.

What If My Data Is Delimited by Something Other Than Single Space?

No worries! SAS gives you the flexibility to import text files delimited by almost any character—not just spaces. You simply need to adjust the DELIMITER= option within your PROC IMPORT statement.

Below is a handy reference table showing common delimiters and how to specify them correctly in SAS:

DelimiterSymbolDelimiter =
Comma,‘,’
Tab(tab)’09’x
Pipe`‘|’
Semicolon;‘;’
Space‘ ‘
Custom~, ^, #'character' (e.g., '@')

Tips & Troubleshooting

  • Missing Values: Watch out for missing delimiters or short fields—they can shift your data.
  • Preview First: Use a small test file to confirm your import setup before loading huge datasets.
  • Log Warnings: Always check the SAS log for warnings or truncation issues.

If you need help or want tips on importing a CSV file into SAS, I have a how to guide on that here.

Importing a .txt file into SAS using PROC IMPORT is a powerful and efficient way to get started with data analysis. By simply specifying the correct DBMS and DELIMITER options, you can handle a wide range of file formats—from tab-delimited logs to pipe-separated system exports.

Got Questions?

If you run into issues or want help with a tricky file format, feel free to drop a comment below or reach out! I’m happy to help troubleshoot and offer tailored code examples.

Thanks for reading—and happy coding in SAS!

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How to Start a Career in Data Analysis with Programming Skills

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In a world overflowing with data, the ability to make sense of it is one of the most valuable skills you can have. Whether you’re analyzing customer behavior, tracking business performance, or uncovering trends, data analysis is at the heart of modern decision-making.

If you enjoy working with numbers, solving problems, and telling stories with data, and you’re curious about tools like Python, SAS, and Power BI, this guide will walk you through how to kickstart your career as a data expert.

1. Understand What Data Analysis Is

Data analysis is the process of collecting, cleaning, exploring, and interpreting data to help organizations make better decisions. It’s not just about crunching numbers, it’s about uncovering insights.

Data analysts work with tools and programming languages to:

  • Extract and clean data from different sources
  • Visualize data through charts, dashboards, and reports
  • Use statistics to identify patterns or outliers
  • Present findings to stakeholders in a clear, actionable way

2. Pick Your Core Tools: Python, SAS, or Power BI

There are many tools out there, but here are three widely used ones to get started with:

  • Python: A powerful and flexible language used for data manipulation, analysis, and visualization. It has libraries like Pandas, NumPy, Matplotlib, and Seaborn.
  • SAS: A professional-grade tool used in industries like healthcare, finance, and government for heavy-duty data management and statistical analysis.
  • Power BI: A business intelligence tool from Microsoft that helps users create interactive reports and dashboards. No heavy coding required, but it pairs well with data skills.

Tip: Learn a mix of programming languages such as Python for flexibility, Power BI for business-facing insights, and SAS if you’re targeting corporate or regulated industries.

3. Learn the Fundamentals of Data Analysis

Start with the basics, no matter which tool or language you choose. Focus on:

  • Understanding different types of data (quantitative vs qualitative)
  • Data cleaning and preparation techniques
  • Descriptive statistics (mean, median, mode, standard deviation)
  • Data visualization (charts, graphs, dashboards)
  • Asking good questions that data can answer

Online platforms like Coursera, DataCamp, Khan Academy, and YouTube offer beginner-friendly courses tailored for aspiring data analysts.

4. Practice with Real-World Datasets

Once you’ve learned the fundamentals, start practicing with public datasets. Sites like Kaggle, UCI Machine Learning Repository, and data.gov are treasure troves of real-world data.

Here are a few beginner project ideas:

  • Analyze COVID-19 trends
  • Visualize sales data for a fictional company
  • Explore demographic data from your city or country
  • Build a dashboard that tracks social media engagement

The key is to turn raw data into a story or insight someone could use to make a decision.

5. Build a Portfolio

As you work on projects, document them and share your work. A strong portfolio is often more valuable than a resume, especially when you’re just starting out.

Include:

  • A short write-up explaining the goal of the project
  • Screenshots or links to visualizations (especially with Power BI dashboards)
  • Code snippets or full notebooks (if using Python or SAS)
  • Insights or recommendations you found

Platforms like GitHub, Medium, or a personal blog are great places to showcase your work.

6. Learn to Communicate Data

Data is only valuable if others can understand it. As a data analyst, your ability to tell a story with data is just as important as your technical skills.

Practice:

  • Writing clear, concise summaries of your findings
  • Creating clean, well-labeled charts
  • Presenting your results in slides or reports

Tools like Power BI or Tableau are especially good for building dashboards that speak to non-technical audiences.

7. Look for Entry-Level Roles and Certifications

Once you’ve built up your skills and portfolio, it’s time to start applying. Look for roles such as:

  • Data Analyst
  • Business Intelligence Analyst
  • Reporting Analyst
  • Junior Data Scientist

Certifications can help you stand out too:

  • Microsoft Power BI Data Analyst Associate
  • Google Data Analytics Certificate
  • SAS Certified Specialist
  • IBM Data Analyst Professional Certificate (Coursera)

Final Thoughts

Becoming a data analyst doesn’t require a Ph.D. or decades of experience. If you’re curious, analytical, and willing to learn how to write a bit of code, you already have the foundation.

Start small, stay consistent, and build real projects. With tools like Python, SAS, and Power BI, you’ll be well-equipped to transform data into decisions and carve out a fulfilling career in a data-driven world.

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Coding for Creative Minds: How to Use Imagination When Learning to Code

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When most people think of computer programming, their minds often jump to logic, math, or long strings of confusing symbols on a dark screen. While these technical elements are certainly part of the picture, they only tell half the story. At its heart, programming is an incredibly creative act, one that requires imagination, intuition, and innovation just as much as it does syntax and algorithms. This post dives into the heart of coding for creative minds.

Creativity Isn’t Optional, It’s Essential

Programming is about solving problems, and problem-solving is inherently creative. Whether you’re designing a new feature for an app, building an interactive game, or creating a data visualization tool, you’re inventing something that didn’t exist before. You’re making decisions not just about how something works, but how it feels to use, how readable the code is for others, and how it can adapt to change.

Just like a painter chooses their colors and brushstrokes, a developer makes stylistic choices in architecture, naming, structure, and logic. Creativity is what turns code from a functional mechanism into an elegant solution.

Code as a Canvas

Think of programming languages as artistic tools. R is like a sketchpad for statisticians and data scientists: expressive, flexible, and perfect for quick, insightful visualizations. Python is like a Swiss Army knife for creators: readable, adaptable, and powerful enough to build everything from machine learning models to web apps. SAS is more like a precision drafting table: structured, reliable, and built for handling massive amounts of enterprise data with consistency. Each language has its own style and strengths, and just like an artist picks the right medium for their vision, a developer chooses the right language to bring their ideas to life.

Creative Thinking in Debugging and Optimization

Creativity isn’t just about building new features or visual elements. It plays a huge role in debugging and optimizing code. Tracking down an elusive bug is a bit like being a detective: it takes pattern recognition, curiosity, and the ability to think outside the box. The best developers don’t just follow a formula, they invent new approaches to make their code faster, cleaner, and more efficient.

Collaboration and Innovation

Creative programmers thrive in collaborative environments where ideas bounce freely. Just like writers workshop their drafts or musicians jam to discover new riffs, developers brainstorm, prototype, and iterate. Hackathons, open-source projects, and coding communities are all places where creativity fuels breakthroughs.

Coding for Creative Minds

Want to bring more creativity into your coding practice? Here are a few tips:

  • Experiment: Tinker with side projects or explore domains outside your comfort zone, like game design or creative coding.
  • Read Others’ Code: Just like reading great writing, looking at open-source projects can inspire new ideas and approaches.
  • Break the Rules (Intentionally): Try solving a problem in an unconventional way. Even if it doesn’t work, you’ll learn something new.
  • Cross-Pollinate: Draw inspiration from other disciplines such as music, design, philosophy, architecture. They all offer unique perspectives that can inform your coding practice.

Final Thoughts

Programming isn’t just for engineers or mathematicians. It’s for artists, thinkers, dreamers, and builders. The best code isn’t just functional, it’s beautiful, clever, and a joy to engage with. So next time you sit down at your keyboard, remember: you’re not just writing code. You’re crafting something meaningful. You’re creating.

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How To Import a CSV File in SAS Using PROC IMPORT

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When diving into data analysis, building reports, or prepping data for modeling, one of the most common and essential tasks you may run into is to import a CSV file in SAS.

CSV files are one of the most common ways to store and share structured data. Whether you’re dealing with sales reports, survey responses, or lab results, chances are your data will come in CSV format. That’s why knowing how to import these files into SAS is a foundational skill for any data analyst, statistician, or data science student.

By using PROC IMPORT, you can quickly bring external data into your SAS environment and get straight to analysis — no manual data entry or format conversion required.

In this post, I’ll walk you through step-by-step how to import a CSV file in SAS, plus a few tips and tricks to avoid common errors.

What Each Line Means:

  • DATAFILE: Path to your CSV file.
  • OUT: Name of the SAS dataset that will be created.
  • DBMS=CSV: Specifies that the file is a CSV.
  • REPLACE: Allows overwriting the dataset if it already exists.

Tips to Import a CSV File in SAS

✅ Make sure your CSV file doesn’t have extra blank rows at the top.

✅ Keep column names short and simple (avoid spaces and special characters).

Bonus: View the Imported Dataset

Once your data is imported, you will want to validate that the data was imported as expected. This can easily be done with a PROC PRINT step like the one pictured below.

This PROC PRINT produces a snapshot of the imported dataset, Climate_Disease_Data, and the Obs=10 statement limits the PROC PRINT to shows only the first 10 rows of your dataset. The dataset pictured below shows the output.

Importing a CSV file into SAS is one of those foundational skills that will make your data analysis workflow faster and more efficient. Follow the steps above, and you will have CSV file data at your disposal in no time.

Have questions or want help troubleshooting when you import a CSV file in SAS? Drop them in the comments or reach out — happy to help!