DS Manifesto
Self-taught data scientist shares insights: focus on basic coding, project-based learning, and realistic expectations. Avoid overemphasis on advanced math and gatekeeping obstacles for a successful career.
The insights provided resonate well with my own experience. I found that a solid grasp of basic programming concepts and applying them in practical projects significantly boosted my confidence in the field. Emphasizing project-based learning allowed me to build a portfolio that spoke louder than theoretical knowledge alone. Though advanced mathematics is relevant in some specialized areas, focusing on practical skills and building a strong foundational understanding has been crucial for me. It is important to remember that each data science journey is unique, and personalized strategies often lead to greater success.
i been there, kinda feel its more bout practicall experiments than nailin every theorem. playing with real projects kept me on track, even if i screw up sometimes. learning ds is messy but keeps you growin.
hey guys, i totally agree- messy ds projects can yield unexpected wins. i sometimes find that a small practical experiment opens up more creative insights. has anyone else noticed breakthroughs during seemingly failed prototype runs? lets chat about how our ‘mistakes’ reveal hidden learnin opportunities.