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.
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.
In this post, we’ll walk through several types of data science projects, including data visualization projects, data cleaning projects, and machine learning projects, and identify good places to find datasets for each.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
Why should you learn R programming when you're aiming to learn data science? Here are six reasons why R is the right language for you.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.