Struggling with Data Science? 5 Common Beginner Mistakes

Aspiring data scientists often hinder their progress by making common mistakes, such as neglecting the fundamental mathematics that underpins all models and algorithms. Many beginners get stuck searching for the "best" course instead of taking action, or they fall into "tutorial hell" by not applying knowledge through independent, project-based learning. It is also crucial to prioritize the quality of projects over quantity, moving beyond simple dataset exercises to more in-depth work like building algorithms from scratch. Finally, newcomers should avoid jumping directly to advanced AI and LLMs, and instead focus on mastering evergreen fundamentals like regression and decision trees first.