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Why Python for Data Science? - Guidance Point


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Why Python for Data Science? - Guidance Point

The data science programming necessitates a very handy and manageable language that is simple to read and write yet can supervise highly complex mathematical processing. Python is the best programing language for general as well as scientific computing. Moreover, it is regularly being updated with new additions.

Now we are gonna discuss these Python resources which makes it the favored language for data science.

  • ·         A simple and very easy yet effective language that shortens the code the most than any other language.
  • ·         Its simplicity makes it healthy to handle tuff cases with the minimum use of codes and confusion.
  • ·         It works on the cross-platform phenomenon so that the same code can work in several conditions without the need for any modification.
  • ·         Executes the functions much faster than other languages such as R & MATLAB.
  • ·         Huge memory management capacity particularly its garbage store makes it handy to manage the amount of transformation, cutting, data visualization with great volume.
  • ·         Packages are there in Python that can immediately use the code of other languages such as Java or C. 

Why learn Python for Data Science?
Undoubtedly Python is the best fitting language for any data scientist. I have listed some of the points which will help you to understand why there is a huge craze of Python in Data Scientists.

Python is free, manageable and open source language. It reduces development time almost to half margin with the help of its simple and very effective syntax of Zero with Python.

One can perform the manipulation process, analyze and visualize the data as really powerful libraries are provided under this for machine learning and other calculations related to science. 

Is Python for Data Science only?
Actually, there are two things which you need to know if you are going to opt for this language.

First, Python is a usual-purpose programming language and is not at all limited to just Data Scientists. This also means that one does not need to learn each and every part of this in order to be a great scientist and at the same time on another hand, if you grasp the basics of this programing language you will also be able to understand other languages.

Secondly, Python in terms of CPU time it is not the most effective or efficient language on the planet but it was made to be simple.

So what one do losses in the CPU time win in the engineering time.
Best Python Data Science Frameworks
Numpy

  • ·         Numpy is the abbreviation of Numerical Python.
  • ·         The most popular building and base for high-level tools.
  • ·         Its deep knowledge helps in the use of Pandas for data science.
  • ·         It's really handy and very simple.
  • ·         Its a standard library for scientific computing with really compelling tools for an alliance with C and C++.

SciPY

  • ·         It is an open source library for the sum of various modules.
  • ·   Image processing, integration, interpolation, specific functions, optimization, algebra linear, Fourier transform, clustering are the various modules.
  • ·         This particular library is used with NumPy to run an efficient mathematical calculation.

SciKit

  • ·         This library is used for machine learning in data science. 
  • ·    Various classifications regressions and clustering algorithms implement support vector machines naive Bayes and local regression.
  • ·         SciKit is designed to interoperate with SciPY and NumPy.

Pandas

  • ·         Pandas provide data frames in Python.
  • ·         It is a very strong library for data analysis, compared to other domain-specific languages such as R.
  • ·         It's really easy to handle missing data.
  • ·         It also supports the automated alignment of data.
  • ·         It also contributes tools for data analysis like merging, modeling or cutting of data sets.
  • ·         Very useful in working with data associated with time series.
  • ·         Also, provide sturdy tools to load Excel data, files simple, data banks & fast format HDF5