Python Language in Data Science
Python Language in Data Science-
In Data Science there is a very well-known programming language that is Python. Not only in data science but fields like ITO and others have analysed the power of this programming language and the kind of tools, techniques and rich aspects from mathematics, statistics etc. And if you have tracked the market trends, it has been realised that this language has become the choice over the few year, especially for Data Science. Python and R are the languages which are open source, as compared with SAS, beyond that the ease of this language is one of the major reason that Python has become so popular amongst many. Also provides fantastic libraries for Data Scientists.
Factors Influencing Python-
Speed- Python is relatively faster, not as fast as C, C++ but yes it is faster in its own way.
Packages-There are packages that are available, which are been created by other people, so we don’t have to new one all the time rather we can use the already available one too.
Design Goals- the Syntax rules in Python are relatively easy to understand there by it helps in building applications with cons size and readable core base with few lines of code you can achieve a lot of stuff.
Let’s talk about the Scipy Library-
SciPy is a set of scientific and numerical tool for Python, Its currently supports special functions, integrations, ordinary, differential equation (ODE) solvers, gradient optimization and others. SciPy has fully-featured versions of the linear algebra modules and it is built on top of Nympy, which is another type of library in Python.
NymPy- This is the Fundamental package for scientific computing with Python. It contains powerful N-dimensional array object. Tools for integrating C/C++ and Fortran code. NymPy is useful in linear algebra, Fourier transform and random number capabilities.
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