338 words, 2 min read
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Yes, Iâm ready to touch the hot stove. Let the language wars begin.
Actually, the first thing Iâll say is this: Use the tool youâre familiar with. If thatâs Python, great, use it. And also, use the best tool for the job. If thatâs Python, great, use it. And also, itâs Ok to use a tool for one task just because youâre already using it for all sorts of other tasks and therefore you happen to have it at hand. If youâre hammering nails all day itâs Ok if youâre also using your hammer to open a bottle of beer or scratch your back. Similarly, if youâre programming in Python all day itâs Ok if youâre also using it to fit mixed linear models. If it works for you, great! Keep going. But if youâre struggling, if thâŚ
338 words, 2 min read
â ď¸ This post links to an external website. â ď¸
Yes, Iâm ready to touch the hot stove. Let the language wars begin.
Actually, the first thing Iâll say is this: Use the tool youâre familiar with. If thatâs Python, great, use it. And also, use the best tool for the job. If thatâs Python, great, use it. And also, itâs Ok to use a tool for one task just because youâre already using it for all sorts of other tasks and therefore you happen to have it at hand. If youâre hammering nails all day itâs Ok if youâre also using your hammer to open a bottle of beer or scratch your back. Similarly, if youâre programming in Python all day itâs Ok if youâre also using it to fit mixed linear models. If it works for you, great! Keep going. But if youâre struggling, if things seem more difficult than they ought to be, this article series may be for you.
I think people way over-index Python as the language for data science. It has limitations that I think are quite noteworthy. There are many data-science tasks Iâd much rather do in R than in Python.1 I believe the reason Python is so widely used in data science is a historical accident, plus it being sort-of Ok at most things, rather than an expression of its inherent suitability for data-science work.
At the same time, I think Python is pretty good for deep learning.2 Thereâs a reason PyTorch is the industry standard. When Iâm talking about data science here, Iâm specifically excluding deep learning. Iâm talking about all the other stuff: data wrangling, exploratory data analysis, visualization, statistical modeling, etc. And, as I said in my opening paragraphs, I understand that if youâre already working in Python all day for a good reason (e.g., training AI models) you may also want to do all the rest in Python. Iâm doing this myself, in the deep-learning classes I teach. This doesnât mean I canât be frustrated by how cumbersome data science often is in the Python world.
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