Read this guide through the most common data science programming languages and when to use them in data science.
This is a great level of detail and very useful to use if you wonder if your choice was correct or if you should expand into another language.
Because she wanted to create useful, accurate analysis with as little work as possible.
I found this really interesting and it looks very useful too - data preparation if done well can help you to produce much better results from your analysis,
How to calculate the ACF and PACF values from scratch in Python.
This was very clear and really helped me to understand how to calculate them in Python as I'm really not good at that language no matter how hard I try.
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He is a senior software engineer at Google Singapore and very often he is asked about which programming language to learn.
I can agree that learning one is great and that it really depends on what you are working with. Learning the basic concepts via one of them is useful no matter what. Arrays, variables, loops etc are all basic skills that can be transferred across languages.
… but up to 30 times faster.
This looks very interesting to experiment and play with.
An overview of Python’s method for combining strings and variables and why you should use it.
A very useful article and well worth a bookmark.
