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I’ve been working on a new course for the past few months. It is a complete start-to-finish guided project course. We start all the way from setting up a data science environment and gathering data and work our way up to training and tuning a machine learning algorithm.
When I was planning the course, I wanted it to have a solid outcome. So I designed it in a way that if you go through the whole course, you will have a presentable, portfolio-proof project in your hands. But I still wanted it to be on the beginner to intermediate level.
This, of course, came with its challenges. I had to think hard about what to include and what to omit. In data science, it is almost always possible to go one step deeper into the concepts. And it is a natural instinct to want to go deeper into every topic you hear about and every concept you start learning about.
But there is a point where the extra information starts being an inefficient overload, as you don't need it yet and you won't use it anytime soon.
I’ve managed to create a good balance of concepts after a lot of thinking. In the course, we go deep enough to thoroughly understand each concept and learn how to apply them to an actual data science project but not too deep that it starts getting confusing and we lose the bigger picture. I kept in mind that building a foundation is our goal. The focus should be on learning things step by step, and practice as we go. After that, you can easily build on top of it. This is similar to the levels I mention in the data science kick-starter mini-course.
It’s not easy to know when to stop, though. I understand. Many topics in data science are connected. So when you don’t know just one thing, you might be left feeling like you know nothing.
This is something I've dealt with a lot when I was learning data science. I constantly felt like I didn't know enough simply because I didn't know everything. It's ridiculous to say it out loud, I know, but it had actual consequences. I overworked myself, thinking that I was always lacking.
This is your reminder that when you’re learning something new you don’t need to understand everything there is to know about it. Spend your energy cleverly.