How NOT to Build a Data Science Project

How NOT to Build a Data Science Project

The mistakes I have made along the way, and how you can avoid them in your next project. I knew I was doing something wrong. I had built up a repertoire of interesting, practical projects. I had a couple of online courses to showcase what I had learnt. I even built a portfolio website to showcase all my projects and articles (which you can access here).

I knew I was doing something wrong.

I had built up a repertoire of interesting, practical projects. I had a couple of online courses to showcase what I had learnt. I even built a portfolio website to showcase all my projects and articles (which you can access here).

But, I still felt a gaping hole in the knowledge I had. A huge, gaping, chasm-sized hole. I felt like something major was missing from the equation.

That’s when I came across some absolutely amazing playlists on YouTube on building an End-to-End Data Science project from scratch. Some great examples are this one by Ken Jee, this one by Daniel Bourke and this one by Data Professor. (Shout out to all of them for some absolutely brilliant content!)

I realized I needed to get started on an End-to-End Data Science project, stat (pun very much intended). Getting hands-on and diving right in, though uncomfortable at first, is the best way to learn something new.

And so I dove straight in.

Until I got stuck. Again.

And again.

And again.

But finally, after hours of toiling, a couple dozen Stack Overflow searches (thank the heavens for Stack Overflow) and a lot of banging my head against the wall out of frustration after running into a wall of errors (this didn’t help as much), I was done.

You can check out my final project here. It’s a Data Science Salary Predictor (inspired and guided by Ken Jee’s playlist). I’m also working on a couple more of my own, this time with no reference or guidance, to really push myself beyond the perceived limits of my understanding.

During the process of building these projects, I learnt a whole lot, not only from a Data Science point of view, but also about how to tackle the problem of structuring such a project in the first place; by making a lot of mistakes and learning from them, again and again.

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