5 tech skills to stay competitive in 2025

By: Janice Mejías Avilés, Edited by: Marie Custodio Collazo

Published: June 12, 2025

Artificial intelligence (AI), automation, and cloud tools are reshaping how teams operate and who gets hired. In this guide, we break down five technical skills to help you stay competitive in 2025. These insights are based on May 2025 survey data from edX and career coach Holly Lee's insights on how to stay sharp, stand out, and future-proof your path.

Why upskilling isn't optional anymore

Economic pressure is accelerating the pace of change and pushing more professionals to upgrade their skills. AI-driven disruption, combined with growing automation, has left many workers in the U.S. feeling uncertain about their long-term career prospects.

According to a May 2025 survey from edX, 82% of managers and supervisors say workers need to pursue additional education or training at least once a year to remain competitive.

The survey asked workers to mention the skills they believe are most important for future career success. Of the top 10, these five are most directly tied to the tech industry:

• Machine learning and AI
• Data analysis
• Cybersecurity
• Programming
• Cloud computing

Career coach and former recruiting leader at Amazon, Google, Meta, and Microsoft, Holly Lee sees these same priorities reflected in her coaching practice.

"All these skills are in a bubble. They can't function without one another. As long as your heart is in the tech space, you need to understand how an environment works," says Lee. "Certifications in these skills will give you all the framework, and they will give you a boost when applying for jobs."

5 tech skills to consider upskilling in 2025

Each of these skills connects to the others. Together, they form the foundation of today's most in-demand roles. Here's why they matter now, and how to start building them.

Machine learning (ML) and AI

Fifty-four percent of the workers we surveyed say AI and ML skills are important for career stability, but only 4% are actively studying them. Understanding the differences between AI and ML may help you get started.

"You don't start with AI, you start with machine learning. Once you understand data patterns, then you move into AI," Lee says.

To build tools that respond to real-world input, like robotics, automation, or personalization, you need fluency in:

• Advanced math
• Algorithms
• Data patterns
• Data structures
• System patterns

Data analysis

Data analysis is still the foundation of most tech-adjacent roles, from business analytics and marketing to machine learning and data science. A deep understanding and experience with data analytics is often the gateway to specialization in AI or ML.

"Everyone wants to say 'AI,' but everything starts with data," Lee says. "If you want to build machines, predict behavior, or automate systems, you need to know how data patterns work first."

Entry-level roles like data analyst are great starting points, because they teach you how data behaves. That knowledge is transferable across fields, industries, and sectors.

"Even if you're new to tech, start with data. Build momentum from there," Lee suggests.

Cybersecurity

As digital systems grow more complex and AI tools become more powerful and ubiquitous, security becomes essential, regardless of the industry.

"Once you build the tech, you need to protect it. Cybersecurity analysts and engineers are already in high demand, and it's only going up," says Lee.

Programming

Programming ranks as the fourth most important skill (22%) among survey respondents, and Lee agrees. Programming fundamentals power every other skill on this list.

The right language for you to learn often depends on your goals:

• Python is widely used in machine learning, data analysis, and AI.
• JavaScript is essential for front-end development and web-based applications.
• C programming is key for systems-level programming and robotics, especially when working on physical devices or embedded AI systems.
• SQL is the standard for querying structured data and is often required for data analyst roles.

"If a company is building any physical product, it's systems-level programming, and that means C," Lee explains.

Cloud computing

Everything runs in the cloud, from apps to AI tools. Companies need workers who understand how to build, scale, and host these systems.

"Cloud architect is one of the hottest jobs right now, because everything we do is digital. We have to host everything on the cloud," Lee says. "None of these smart tools work without a solutions infrastructure."

These skills aren't a trend; they're transferable. And no matter your background, they show up across job titles, industries, and career stages.

Lee's final advice to workers and job seekers is that as you develop these skills through courses, programs, or certifications, make time to create projects or case studies that show what you know and what you can do.

"Make sure you're working on projects that can be relevant. Stick to the industry you're interested in. Pay attention to the innovation the world is leaning toward and stick with it".