As an aspiring AI engineer, I’m often faced with the dilemma of where to focus my energy: building a solid foundation in data science or diving straight into AI/ML libraries and frameworks. With a background in programming and a basic understanding of linear algebra, calculus, and probability, I’m eager to learn and deploy real-world intelligent systems.
The question is, how much time should I invest in data science fundamentals like data cleaning, EDA, statistics, and visualization versus hands-on experience with AI/ML-focused libraries like PyTorch, TensorFlow, Hugging Face, or LangChain? My ultimate goal is to work professionally in applied AI, building models, integrating them into systems, and contributing to open-source or freelance projects.
To achieve this, I’m left wondering: Is mastering data science essential for an AI engineer, or just helpful in certain roles? Would it be better to start hands-on with AI libraries and fill in data science knowledge as needed? How do AI engineers usually balance their time between theory, tooling, and project-based learning?
I’d love to hear from experienced AI/ML engineers and learn from their approaches. Are there any well-designed learning roadmaps or university course structures that emphasize this specific engineering-oriented AI track?
Any insights or recommended resources would be greatly appreciated.