4 min read

Applications of Python in Data Science

Sep 18, 2020 6:44:20 AM

Data Science is the hottest job of this century and companies irrespective of the domain they work on are expanding their business to support analytics and make meaning insights from the data. Data has become an asset to every organization wherein it helps in taking business decisions and helps in the growth of the organization.

The question arises how to we access or communicate with the data. There comes the use of many analytics tools like Hadoop, SQL, SAS, R programming, Matlab, Java and many more to list out. And not to forget the most popular tool that is being used by the data scientists all over the world is Python. In the recent times, with the evolution of many programming tools Python has gained its own popularity by all the data scientists.

Python is called as the “Swiss Army Knife” of the coding world. It is a dynamic language easily understandable for beginners and also provides interface with the high-level programming languages like C and Fortran. It supports exclusive library files for statistical analysis which provides user friendly access to the data scientist.


  1. Easy and Simple: Python is user friendly and can be easily understood by beginners as well. This coding language has easy syntax and makes it simple to get hands on with.
  2. Scalability: Unlike other statistics tools like R or Matlab, this provides much scalability and supports high volume of data (be it text or image or web development). It works much faster when compared to other tools.
  3. Vast choice of library files: Python provides vast number of library files that makes it easy for data scientist to choose from based on the problem and scenario they wanted to address. Libraries like Pandas, Numpy, SciPy, Statsmodel, matplotlib and Scikit-learn provides you the right set of statistical functions providing better solution.
  4. Integration with Enterprise Applications: Python integrates with high level enterprise tools even when coded by other programming language. This makes it unique as it can understand and interpret other languages like Java, C and C++.
  5. Python Community: As the data scientists across the globe using python you are always open to get solutions from their community. Connecting with Python experts helps you to understand and discover much more through the open communities like StackOverFlow, Reddit and much more.


Data Extraction: The first step of data analysis is the data extraction. Python helps you to scrap the data from any website using Python Scrapy and BeautifulSoup library files. Also, Python supports to import lakhs of rows and columns which other programming language doesn’t support.

Data Cleaning: Not every time data would be readily available for analysis. There comes the use of Python library files like pandas, numpy, prettypandas, Dora and so on to help you out in structuring your data making it ready for analysis

Data Visualization: Finding insights from the data becomes one important step of data analysis. Python provides exclusive libraries to help us out like Matplotlib, Seaborn, Plotly etc for better charts and graphs.

Model Building: The final step of Data Analysis is the model building using Machine learning algorithms which involves complex computations. Libraries like sklearn, NLTK, Tensorflow supports in building high level ML models.

Be it any domain we work on, the most demanded data scientist is expected to have experience working in Python. The necessity of learning Python is increasing day by day to make yourself stand out of this competitive job market.

Why wait? Get Started today and Happy Coding!


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Written by Byte