How can I start a data science project when I'm stuck?

Hello everyone,

I’ve decided to take my Python learning to the next level by attempting my first data science project. Unfortunately, the results were not what I hoped for. I spent the entire day trying to make progress, but I couldn’t even get the basics like setting up packages in VS Code to work. It was really demotivating.

I’ve read several beginner project ideas, like building a fake news detector, but I don’t know where to begin. I’m trying to avoid just following tutorials since that feels like copying and not learning. However, projects like these often need knowledge of areas like machine learning, which I haven’t touched yet.

My Python basics are solid, and I’ve done simple projects, but I feel completely lost with data science. How should I approach these projects to actually learn and not just mimic tutorials? Any advice is welcome.

Start with logistic regression and make sure you understand the basics behind it. It’s a core concept in data science and can help you build a foundation.

It seems like what you need most is structure. A good starting point might be exploring data using pandas and creating visualizations. You could try websites that offer guided data challenges or projects. These can help you get a sense of how to work with data step by step.

Machine learning can feel overwhelming at first since it’s such a vast field. Maybe start small by exploring datasets and asking simple questions like ‘What does this data show?’ before diving into advanced topics.

@Lin
Thank you for pointing that out. Structure is exactly what I’ve been missing. I really appreciate your advice and will try to organize my approach better tomorrow. Thanks again for helping me see things more clearly!

Kaggle is a great platform to start with. You’ll find datasets and beginner-friendly projects there.

Learn these tools step by step:

  • numpy: For working with arrays and numerical operations.
  • pandas: For handling and analyzing structured data.
  • matplotlib/seaborn: For visualizing data.
  • sklearn: To explore machine learning basics.

Mastering these will give you a good foundation for data science.

@Zayne
Thanks for breaking it down. This gives me a clear path forward. I’ll start with pandas and numpy and build from there.

Pick a simple idea, start coding, and learn as you go. Read the documentation of the tools you use; it’s really helpful.