從零開始成為數據科學家：學習路線、困難與求助

From Complete Beginner to Data Scientist:
My Roadmap, Struggles, and Where I Need Your Help!

Hello DEV Community! 👋

I am officially embarking on the journey from a complete beginner in Data Analysis to an advanced Data Scientist. Like many starting out, I am staring down a massive mountain of tools, languages, frameworks, and theories—and honestly, it's a bit overwhelming.

I wanted to share my current plan, outline where I'm stumbling, and ask those of you already in the trenches for your advice and guidance.

My Current Learning Roadmap
Right now, I am focusing on building a solid foundation before diving into machine learning and advanced modeling. Here is what my current learning stack looks like:

Foundations: Python, SQL, and Excel for data wrangling.
Analysis & Visualization: Pandas, NumPy, Matplotlib, and Seaborn (along with learning Tableau/PowerBI).
The Next Frontier (Where I need advice): Statistics, Machine Learning algorithms, and MLOps.
Where I'm Stumbling (And Need Your Help)
The hardest part so far isn't learning the syntax of Python or writing a complex SQL JOIN—it's knowing what matters.

Tutorial Purgatory: I find myself completing tutorials easily, but when I open a blank Jupyter notebook with a raw dataset, I freeze. How did you break the habit of relying on step-by-step tutorials?
Math Anxiety: How deep do I really need to go into linear algebra and calculus before building machine learning models?
Portfolio Projects: Everyone says "build a portfolio," but what separates a generic "Titanic dataset" portfolio project from one that actually catches an employer's eye?
Let's Connect and Learn Together
If you are a seasoned Data Scientist or Data Analyst, or if you've recently made this exact transition, I would love to pick your brain.

What resource or concept was the "lightbulb moment" that made everything click for you?
If you could restart your data journey today, what would you skip, and what would you focus on harder?
Let me know in the comments below! 👇