5 Must-Have Skills for Statistical Programmers in 2025

Trinath Panda
Sep 7
4 min read

The world of statistical programming isn't just changing; it's sprinting ahead. New tools, new data standards, and new expectations are popping up constantly. If you want to not only survive but thrive in 2025, you need to adapt.

Forget just "keeping up." It's time to get ahead of the curve. Here are the five non-negotiable skills that will make you an indispensable statistical programmer in the years to come.

Key Takeaways for 2025

Version Control is Mandatory: Master Git and GitHub for collaboration and reproducibility.
A New Data Standard is Coming: Prepare for the shift from SAS XPORT to the more modern Dataset-JSON.
Data is Everywhere: Skills in Real-World Evidence (RWE) are critical as the industry moves beyond traditional trial data.
Go Multi-Lingual: Proficiency in Python and R alongside SAS is now the expectation.
Expertise Matters: Deep, nuanced knowledge of CDISC standards is a key differentiator.

1. Master Git & GitHub

Why use GitHub for statistical programming? Git and GitHub are essential for version control, collaboration, and regulatory compliance, replacing outdated file-naming chaos like analysis_v3_final.sas.

Benefits of Git and GitHub

Collaboration: Work seamlessly with multiple programmers.
Traceability: Track code changes for audits (70% of pharma companies use GitHub for clinical projects).
Backup: Safeguard your work.
Reproducibility: Recreate analyses anytime.

This isn't just a trend. Pharmaceutical companies like GSK, Roche, Sanofi, Novartis and many more are now using Git, knowing how to manage repositories and handle merge conflicts is as fundamental as writing a `PROC SORT`.

I have written a small and crisp post on Why Git is the future. Check this post to know more.

2. Get Fluent in Dataset-JSON: The New Language of Data Submission

For decades, the SAS XPORT format has been the standard. But with the FDA actively evaluating Dataset-JSON as its replacement, a major shift is on the horizon. This isn't just a minor format change—it's a complete modernization of how we share clinical data.

What is Dataset-JSON? It’s the FDA’s proposed replacement for SAS XPORT, a lightweight, API-friendly format for clinical data submissions, set to dominate in 2025.

Why you need to learn Dataset-JSON now:

API-Friendly: Integrates with modern web tools.
Efficient: Smaller file sizes than XPT.
Flexible: Supports JSON and NDJSON.
Future-Ready: Designed for cloud data exchange.

Getting ahead of this shift will give you a massive competitive advantage when Dataset-JSON becomes the new gold standard.

Don't miss my post on the future of data exchange. Click here

3. Dive into Real-World Evidence (RWE): Data Beyond the Clinic

The pristine, controlled environment of a clinical trial is one thing, but how does a drug perform in the messy, unpredictable real world? That's the question Real-World Evidence (RWE) and Real-World Data (RWD) are here to answer. A study analyzing FDA approvals from January 2019 to June 2021 found that 85% of new drug and biologics license applications incorporated RWE in some form, with the proportion increasing each year.

This is a monumental shift. We're now drawing insights from electronic health records, insurance claims, and even patient wearables to get a complete picture of a drug's impact.

Why RWE Is Critical

Faster Approvals: Supports FDA’s Real-Time Oncology Review.
Cost Savings: Reduces reliance on expensive trials.
Real-World Insights: Shows drug performance in diverse populations.

4. Python and R: The Essential Programming Duo

SAS is still a cornerstone of regulatory submissions, but the industry is rapidly embracing a multi-language approach. To be a top-tier statistical programmer today, you need to be fluent in more than just SAS. Python and R have moved from "nice-to-have" to "must-have" skills.

Why learn Python and R for statistical programming? While SAS is still used, Python and R are critical for automation, visualization, and modern clinical workflows.

Companies like Roche, Novartis, and GSK are all-in on using R and Python. Being "bilingual" or even "trilingual" (SAS, R, Python) makes you an invaluable asset to any team.

5. Advanced CDISC Standards: Beyond the Basics

Knowing the basics of SDTM and ADaM isn't enough anymore. The CDISC standards are evolving, becoming more complex and more deeply integrated with modern technology. Simply following the rules is one thing; understanding the why and leading the implementation is another.

Deep CDISC knowledge helps companies avoid costly delays and frustrating queries from regulatory agencies. Programmers who are true experts become the go-to leaders on their teams.

Frequently Asked Questions (FAQ)

Q: Is SAS becoming obsolete for statistical programmers?

A: No, SAS is not obsolete, especially for regulatory submissions where it remains the industry standard. However, its dominance is being complemented by open-source languages. A modern programmer is expected to be proficient in SAS *and* have skills in Python or R for tasks like automation, visualization, and machine learning.

Q: I'm a beginner. Which skill should I learn first?

A: If you are new to programming, start with the fundamentals of a language like SAS, Python or R, as they are versatile and have a wealth of learning resources. If you are already in the industry, mastering Git and GitHub will provide the most immediate impact on your daily workflow and collaboration.

Q: How important is Real-World Evidence (RWE) for a junior programmer role?

A: For a junior role, deep RWE expertise may not be required, but demonstrating awareness and a basic understanding is a huge plus. Knowing the difference between RWD and RWE and being familiar with the challenges of handling observational data will make you a much stronger candidate.

Q: What are the top skills for statistical programmers in 2025?

A: Git/GitHub, Dataset-JSON, RWE, Python/R, and advanced CDISC standards.

Q: Why is GitHub important for programmers?

A: It enables collaboration, traceability, and reproducibility, used by 70% of pharma companies.

Q: What is Dataset-JSON used for?

A: It’s a lightweight, API-friendly format replacing SAS XPORT for clinical data submissions.

Q: How does RWE help drug development?

A: RWE speeds approvals, cuts costs, and provides real-world insights, used in 70% of NDAs.

About the Author: Trinath is a clinical data scientist who turns complex trial data into clear, compliant, and actionable insights. Certified in CDISC SDTM and fluent in SAS, Python, and ADaM standards, he’s passionate about data quality, audit readiness, and making clinical research smarter and faster.