I read a post on LinkedIn the other day which talked about how someone had been coding Python for 10-years but still looks up some basic functions every day.
I've been a machine learning engineer for 8-months and I do the same.
If you could see my Stack Overflow history, you’d find a bunch of things which you'd expect to find in the first chapter of a book on Pandas.
It's my own fault. I could take the time and learn all the functions off by heart. Then I wouldn't have to look them up every time.
But what happens when they change? Or if the library gets updated?
It's hard to change a way of doing things if it’s the way you've always done it. So learning a programming language off by heart may be helpful but it could lead to problems down the road.
I'm a fan of learning what you need to learn when you need to learn it and not being deterred by your previous learnings.
This kind of firey curiosity is extinguished in school. Instead of crossing knowledge roadblocks when they come, the curriculum tries to prepare you for every single one.
What's more important than knowing what to do in every situation is knowing how to figure out what to do. Knowing how to ask questions, knowing how (and being willing) to be wrong.
So when does it all start to make sense?
Someone asked me this the other day. They had been coding Python and working on a few projects but still running into a few struggle points.
I replied back with my experience of things still not making sense at times and an answer similar to the above.
I prefer things not to make sense every so often. If everything made sense, the world would be a pretty boring place.
I'm not a fan of boring. And I know you aren't either.
Creating and deploying data science/machine learning pipelines on the cloud still doesn’t make sense to me. But I'm getting there. The Data Engineering on Google Cloud Specialization on Coursera has been helping. Part 4 was all about streaming data. I talk about it more in my latest video.