We agree it is simple, but actually, it isn’t so much Python per se, it is some pretty cool libraries (SciPy, in particular) that do all the hard work.

Identifying causal relationships from observational data is not easy. Still, researchers are often interested in examining the effects of policy changes or other decisions. In those analyses, ...

It is a handy tool for keeping a record of data explorations, creating charts, styling text and sharing the results of that work. For data analysis, the cornerstone package in Python is “Pandas”.

Find out what makes Python a versatile powerhouse for modern software development—from data science to machine learning, systems automation, web and API development, and more.