Life is short, learning is limitless?
The proverb is: Life is short, learning is limitless. Most people consider the motto as an encouragement to study lifetime. However, the original sentence is 吾生也有涯，而知也无涯。以有涯随无涯，殆已！已而为知者，殆而已矣！and there are two sentences behind are not translated. It says that if you use your short life to chase the limitless learning, you will eventually fail. If you know it and still trying to learn everything, you will be more tired. Adding these changes the meaning to the opposite. Indeed, in this information booming era, information generates much faster than ever. There is useful knowledge, but most of them are not that useful and profound.
My friend asked me whether your favorite team win a game matters to you, and I think this in two ways: most of the time, that kind of information does not matter to you, and it will serve as a quick talk material, or even not. On the other hand, sometimes things like that could have psychological effects on you, such as cheer you up. When I watched Kobe Bryant’s last game, I was wordless: it should only happen in a Hollywood movie, that the old elite player fought through the last game and scored the decisive points. There are some kinds of spirit that I could see from it, and it is no longer merely a piece of information.
Back to the learning topic. In my study area, there are so many things that you could do, and you only have limited time and resources. How to come up with something that is reasonable and achievable based on your situation become more critical. In the field of Data Science, people tend to become full-stack, meaning that you can work alone form data collection, data cleaning, machine learning modeling and generate results. However, if you consider this to be the responsibilities of a full-stack data scientist, you are mostly wrong. There are lots of unanswered questions, such as Where your data comes from, what is the purpose of doing the whole thing, what is your output result and how to use the outcome, etc. You are not working alone, unlike a Kaggle project. Find what you are most interested, and what you are good at, then you can either strengthen your advantages or extrapolate to the nearby subject.
Studying lifetime is a great goal, but with your focus, the studying process would become more organized, quick and successful.