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www.sharpsightlabs.com | ||
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accessibleai.dev
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| | | | | In this article I'll introduce you to Anaconda and show you how get a standardized Python data science environment up and running on your machine in minutes. | |
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jaketae.github.io
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| | | | | In this post, we will continue our journey down the R road to take a deeper dive into data frames. R is great for data analysis and wranging when it comes to dealing with tabular data, especially thanks to the dplyr package, which is R's equivalent of Python's pandas. | |
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data36.com
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| | | | | Learn data science and machine learning in Python, pandas and scikit learn! This is a free series of 20 in-depth tutorial articles. | |
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vickiboykis.com
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| | | When I'm working with Jupyter notebooks, I often want to work with them from within a virtual environment. The general best practice is that you should always use either virtual environments or Docker containers for working with Python, for reasons outlined in this post, or you're gonna have a bad time. I know I have. The workflow is a little long, so I thought I'd document it for future me here. | ||
