Summary
I share my journey as a self-taught data scientist and analyst. Focus on practical coding, real projects, and smart problem framing over mastering every theory. Overcome gatekeeping; keep learning.
hey flyingeagle, love ur style. i started small with real tasks and embraced my mistakes along the way. no theory overload for me, just experimenting and learning on the fly. keep it real!
hey flyingeagle, luv ur journey its so inspirng! i wonder how did u choose projects that bridged theory & practice? any pitfalls u encountered along the way u wanna share? it’s really cool how real work trumps just textbooks sometimes.
FlyingEagle, your narrative on practical learning resonates strongly with my own experiences. I found that engaging directly with real-world tasks not only builds competence but also exposes the nuances that textbook theory often overlooks. Early in my journey, applying concepts in projects allowed me to identify gaps that theory alone could not fill. Although revisiting fundamentals was occasionally necessary, the iterative process of experiment and reflection ultimately fortified my understanding. This applied aspect of learning has proven critical in both problem-solving and intuitive decision-making in data science.
hey flyingeagle, so curious! your hands-on style really reson8tes with me. which project taught you most in learnng, and did any surprise challenges steer u in a new direction?