Learning by doing and having to build real projects is hard, but skill development has always required hard work.

At Qwasar, we intentionally design programs around project-based and competency-based learning. Students spend their time working (and sometimes struggling) through real software problems, debugging real systems, and figuring out how to move forward when things aren’t obvious. That process is where learning actually happens.

If you're serious about becoming a strong software engineer, you’ve probably asked:

Should I learn C or C++?

This isn’t just a beginner’s question. It’s a foundational one. The decision shapes how you think about memory, performance, abstraction, and system design.

At Qwasar Silicon Valley, we care less about trendy stacks and more about engineering depth. So let’s break this down.

We sat down with one of our students, Harold U., to share more about the app he developed, FlipScan. We want to give you an idea of the depth and breadth of who is in our learning community.This interview is part of ongoing learner interviews with Qwasar students and alumni.

Tony is a Master’s of Science in Computer Science student who’s using his capstone project to tackle one of the most complex and fast-evolving areas in tech: quantum computing.

For his final project, he designed and developed an entire educational platform and simulation environment from scratch. It’s a full-stack system that teaches quantum fundamentals, runs real-time circuit simulations, and (eventually) connects to APIs from IBM, Amazon, and Azure to submit real quantum jobs.

Students in our Master’s program can choose their capstone subject, enabling them to customize their specialization (subject to Qwasar approval). Capstones and thesis projects are always great topics for interviews for the next 3-7 years, meaning the goal of the project is that it serve the student well beyond graduation.

To give an idea of some of the capstone projects students complete, we’ll share more about what Tony built for his capstone.

At Qwasar we’re known for challenging learners to build databases, simulate distributed systems, and master modern backend technologies but sometimes, a foundational lesson in engineering thinking comes from something simpler, like a probability puzzle involving goats.

Enter the Three Doors Problem, one of our most popular collaborative coding sessions. It’s hands-on, open-ended, and deceptively deep. On the surface, it’s a game show scenario. Underneath? It’s a logic challenge, a modeling exercise, and a crash course in statistics, all wrapped into one.

This kind of session captures what Qwasar is all about: collaborative problem-solving, simulation-driven learning, and building your reasoning as much as your technical skills.

 

At Qwasar, we don't just teach software engineering: we train future engineers to solve problems that matter in the real world. In an effort to bring the real world into our Master’s curriculum, we created “Engineering Labs” - a virtual software engineering lab, similar to an architect’s studio or a hardware engineer’s on-site lab.

As with a traditional on-site lab, students are given a project that requires hands-on work to build a finished product. Labs are also an opportunity to work with tools used on the job by engineers and developers that may not be covered in the curriculum.

One standout example? Our Vector Database Engineering Lab, a Level 10 project that blends machine learning, distributed systems, and modern backend engineering into one powerful, practical challenge.

The task: design and implement a scalable, fault-tolerant vector database capable of performing fast similarity searches in high-dimensional space

Few courses currently exist on Agentic AI, and those that do focus on what it is, how it works, use cases, or the theory and set up of the machine learning. But great engineers know that the real challenge is learning how to actually build these systems - as with any software, knowing theory is nice but actually building infrastructure and products is what’s hard.

As a modern college of engineering focused on applied learning, we've built a fully applied Agentic AI course where you'll actually build agents and multi-agent systems in a cohort with other learners. You can apply to be an Alpha Tester of this new course!

How to Earn a Master’s in Computer Science Without a Bachelor’s: Exploring Non-Traditional Pathways

Are you looking to break into tech or advance your career with a Master’s in Computer Science but don’t have a Bachelor’s degree in the field? The traditional route to a CS Master’s typically requires a Bachelor’s in Computer Science or a related discipline, making it difficult for career-changers or those from different academic backgrounds to enroll. However, emerging programs like Qwasar’s College of Engineering offer an alternative approach, focusing on hands-on skills and flexibility without requiring a Bachelor’s in CS.

Below, we’ll compare some traditional paths with Qwasar’s unique program to see how you can earn a Master’s without starting over.

Why Decision-Making Skills Are Critical for Software Engineers

In the field of software engineering, decision-making is more than just a skill—it’s a core competency. Each line of code, every architectural choice, and all tool selections impact the immediate success of a project and its future scalability and maintainability. Effective decision-making enables software engineers to navigate complex technical challenges, work seamlessly with teams, and deliver high-quality, adaptable software that stands the test of time. Let’s dive into why decision-making is so crucial for software engineers, the types of decisions they make, and practical ways to strengthen this skill.

The gender gap in technology, particularly in fields like Artificial Intelligence (AI) and Machine Learning (ML), remains a persistent issue. Despite the rapid growth of these industries, women, and many other people groups, are still significantly underrepresented. This lack of diversity has far-reaching consequences, influencing the development of the technologies that are shaping our future.

Many professionals, including our co-founder, Jennifer, have voiced increasing frustration with the lack of female machine learning engineers in the workforce, and the lack of female presenters and thought leaders at AI/ML conferences. In spaces where innovation is key, the absence of diverse perspectives is both a seriously large missed opportunity and a reflection of the ongoing challenges women face in tech.

Now is the time for change. With AI/ML poised to transform industries, it’s crucial for women to be part of this conversation.