Python for Data Science Made Simple

python for data science

Python is  a powerful programming language and there are several reasons why Data Scientists like python.  Many data  scientists  choose Python because it is a high level programming language that can be found across different teams in the organization. Therefore, Python has become a programming language that connects the different units across the enterprise and a direct channel for data sharing and processing language. Data scientists have another choice of R language , but python can now handle.

Python language is easy to learn also. Python usage is increasing  in data science and had situated in opposition to R language.  Mostly data scientists like to code in python because of following  

  1. Simple syntax
  2. Growing data analytics libraries
  3. Easy to learn
  4. Scalabiliity
  5. Visualization/graphics
  6. Professional computing language

One of the main reasons why data scientists choose is because of its core libraries. Here are some libraries

Numpy  stands for Numerical Python  is additional support for python which supports large multi dimensional arrays, matrices and provides an assortment of high level mathematical functions to perform on these arrays.

Skipy  stands for Scientific Python which is a open source library for python and it contains a modules for optimization , linear , algebra , integration , specialfunctions etc.

Pandas  is a open source library for python written for python programming language for powerful data manipulation  and analysis.

Matplotlib  is a 2D plotting library for python programming language which generates data visualiations such as histograms, power spectra, bar charts and scatterplots with just a few lines of code.

Scikit-Learn  is a software machine learning library for python programming language which is built on Numpy , Scipy and Matplotlib for data mining and data analysis.

Instead of investing lot of time and effort for writing complicated code , the packages of python allows you to focus on essential tasks at hand and study data closely and rapidly.

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