Why Python for Data Science?

Why Python for Data Science?

The experts in Data Analytics domain know that Python is must language to learn if you are thinking to work for Data Science. As we are moving towards the future, the IT industry is experiencing continuous growth. Due to which there is a boom in the demand of the professionals specializes in Python and skilled Data scientists.

Python because it is one of the emerging and one of the popular language which is getting utilized more in the application of data science.

Take an example of multi-tech company Google; the company has developed a deep learning framework known as “Tensorflow.” In this framework, python is the main language behind the development of this framework. Its footprint is also rising in the environment developed by Netflix. Moreover, Facebook and other platforms like Khan Academy utilizing python as the essential language in their environment.

Further, this article will help you to learn basic knowledge about Python, ways to examine data and its use for the creation of beautiful visualization.

The blog is about “Data Science Python” and blog holds following topics

  • Why Data Science suited best with Python?
  • Introduction of Python.
  • Basics of Python.
  • Libraries of Python.

Let’s Start:

Why Data Science suited best with Python?

There is no doubt that Python is the best-fitted language for the Data Scientist. There are some points listed below that will help you to gain an understanding of “Python as people choice for Data Science.”

  • Python is a powerful open source language; also it is free and flexible.
  • Python is known for its easy to read syntax; also it cuts development time in half.
  • Python allows you to perform visualization, data manipulation, and analysis.
  • The most crucial factor about Python is its libraries. It holds powerful libraries for various scientific calculations and machine learning.

And the best part is, the data scientist job is one of the highest paid jobs and on can earn approx $130,700/annum.

Introduction of Python

Python origin occurred in 1989,  and Guido Van Rossum was the person who created Python. The access of Python is entirely free, and it is compatible to run on all platforms. Moreover, Python is an interpreted language with effective semantics. Python is:

  1. Object Oriented
  2. Procedure Oriented
  3. Easy to Learn
  4. High-Level Language

Python Basics For Data Science

Presently, it is the time to involve yourself in programming. But before doing that, it is necessary that you must have basic knowledge of these following topics:

  • Variables: First topic is dedicated to variables because it helps in referring to the reserved memory location for storing the values — also, no need to define variables before utilizing them or before declaring their types.
  • Functions: The functions help to divide code into valuable blocks, that allows you to manage the code and make it reusable, readable, also save some time.
  • Data Type: The Python includes the following data types Lists, Numerics, Strings, Sets, Tuples, and dictionary. These data types are supported by Python, and it defines various possible operations over the variables and storage method.
  • Operators: In the python, operators help in manipulating the value of operands and operators that are included in the Python are comparison, logical, bitwise, assignment, arithmetic, Identity, and membership.
  • Conditional Statements:  It helps in executing the statement sets based on condition, and these conditional statements are – if, elif and else.
  • Loops: The loops help in repeating small parts of the code and to perform it there are three types of loops named as for, while and nested loops.

Libraries of Python

The Python libraries are the actual power of Python when linked with data science. It holds many libraries for various tasks such as analysis, scientific computing, visualizations, etc. Here we discuss some of them:

  • NuMpy: The main library of Python for data science and it is known as “Numerical Python.” This python library is utilized for scientific computing that holds the n-dimensional array object, and it gives tools to integrate C++, C, etc. Also, used for multi-dimensional container regarding general data where you can perform various special functions and NumPy operations.
  • Matplotlib: In Python, Matplotlib is a powerful library for performing visualization. It is utilized for Pythons shell, web application servers, scripts, and different GUI toolkits. Also, various types of plots can be utilized, and multiple plots work with Matplotlib.
  • Scikit-learn: In the python library, Scikit-learn is the main attractions because here you can implement the machine learning using Python. It is the free library that holds simple and effective tools to perform mining and data analysis purposes. Using Scikit-learn, different algorithms like time series algorithm and logistic regression can be implemented.
  • Seaborn: Seaborn is known as statistical plotting library of Python. When using Python with Data science, it will be done using Matplotlib and Seaborn. In Seaborn, there are beautiful default styles and interfaces of high-level for creating statistical graphics.
  • Pandas: In the Python, Panda is the essential library for data science. This library helps in analysis and data manipulation. Also, well suitable for numerous types of data such as ordered and unordered time series, tabular, matrix data, etc.

The landscape of data science is evolving rapidly, and the number of tools for obtaining value from data science is also growing. The languages that are fighting for the first spot are Python and R. The followers respect both languages, and both have their advantages and disadvantages.

But the tech-giants such as Google already shows the way for using Python that making the short and easy learning curve. Also, the Python is inches away to become the best language in the Data science society.  

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