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The unexpected effectiveness of Python in #science. In a keynote on the first day of #PyCon 2017, Jake VanderPlas looked at the relationship between #Python and science. Over the last ten years or so, there has been a large rise in the amount of Python code being used—and released—by scientists. There are reasons for that, which VanderPlas described, but, perhaps more importantly, the growing practice of releasing all of this code can help solve one of the major problems facing science today: https://lwn.net/Articles/724255/

http://www.csestack.org/python-libraries-for-data-science/ As per the DIKW Pyramid Model, #Data_Science job revolves around finding the information, knowledge from Raw Data. And it can be bundled into the stack of 4 entities: source of #data manage and store data analyze the data display analyzed output (#visualization, statistics)

https://github.com/openai/universe #Universe is a software platform for measuring and training an AI's general #intelligence across the world's supply of games, websites and other applications. This is the universe open-source library, which provides a simple #Gym interface to each Universe environment. Universe allows anyone to train and evaluate #AI agents on an extremely wide range of real-time, complex environments.

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf #Scikit_learn is an open source Python library that implements a range of #machine_learning , preprocessing, cross-#validation and #visualization algorithms using a unified interface...