I’m an autodidact in data science with a mechanical engineering background. Key insight: Practical projects and basic skills trump overwhelming prerequisites, proving that hands-on experience is all you really need to excel.
My experience as a self-taught data scientist has shown that practical application combined with theoretical understanding creates a robust learning path. I dedicated time to hands-on projects that directly addressed real-world problems, and whenever I hit a roadblock, I supplemented my knowledge by revisiting fundamental theory. This iterative process not only enhanced my problem-solving skills but also reinforced my confidence in applying concepts in novel situations. Balancing working on projects with ongoing study allowed me to see immediate results while understanding the underlying principles.
hey, loved ur take on self teachin! im curious if u faced any hurdles merging hands-on projecs with ur mech eng background; do u think adding a bit more theory would help? how’s everyone else blending these skills in real projecs?
hey, i think a sprinkle of theory can help but hands-on stuff is where its at. your mech eng knowhow is a device edge, so just keep tweaking and learning on the fly. sometimes simple experiments reveal the best tricks.
hey all, really enlitghtening read! i’m thinkin maybe brief theory bits with hands-on data tasks do wonders. has anyone found a sweet spot between experimenting and studying? would love to hear more of your thoughts!