How can I build CS and AI experience when I feel behind?
Asked by
MellowHarbor42
I'm double-majoring in cognitive science and psychology, and I'm especially interested in AI and developing machine-learning models. I've been taking computer science courses alongside my degree and working on my programming skills, but I feel far behind students majoring in CS. I'm worried that I need side projects and a strong portfolio to qualify for internships or AI research, yet I don't know what to build or where to begin. I've applied to several research opportunities at my school without being accepted, so I'm feeling discouraged and unsure how to make my goals more achievable.
Answers
It’s worth building general programming fundamentals instead of focusing only on AI tools. Practice problem-solving, write small programs, learn how files, processes, and data structures work, and use a language such as Python consistently. You can explore C or shell scripting later if systems programming interests you, but you don’t need to master low-level programming or build an operating system before you’re ready to pursue beginner opportunities.
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You don’t need to build a huge, polished project before applying for opportunities. Start with something small enough to finish, such as classifying a simple dataset, analyzing a public dataset, or automating a repetitive task with Python. The goal is to practice completing projects, documenting what you learned, and improving the next one. A few modest finished projects are more useful than one ambitious project that never gets completed.
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Use one manageable AI project to strengthen your foundations in Python, data structures, statistics, and basic machine learning. Follow a tutorial if needed, but change the project enough that you understand the code and can explain your decisions. Put the code, a short README, results, and what you would improve next in a public portfolio. Also keep applying for internships and research roles—even if you don’t meet every preferred qualification. Applications are part of learning what employers and labs actually expect.
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