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How Data Science Makes Use Of Python For Data Analysis?

Data Science is a multidisciplinary technology that makes use of different technologies like AI, Machine Learning, Predictive Analytics and even Statistical Models. The advanced analytics operations in Data Science also rely on the use of coding with programming languages like Python & R. Most of the Data Scientists are very much comfortable in working with Python for analytics operations like Data Mining, Data Modeling, Data Visualization & such. So, if you are curious to step into the job-role of a Data Scientist then you must surely be having the skills to code with Python.

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Now, let’s take a look at how Python is used in Data Science.

  • Python For Data Collection & Cleansing

Python is having a collection of extensive libraries that will help the analysts or Data Scientists to precisely handle Big Data of any format.  PyMySQL & BeautifulSoup are among the most extensively used Python libraries for web scrapping operations.

  • Python For Data Exploration

DAta Exploration is a crucial step in the business analytics process that helps the Data Scientists in finding answers to most of the challenging business questions. Python supports Data Scientists with libraries like NumPy and Panda which will let them explore data to deeper depths.

  • Python For Data Modeling

Machine Learning algorithms will be playing a crucial role in the data modeling process involving Data Science. Here, in this process, Python supports Data Scientists with libraries like Numpy, SciPy & Scikit-learn to apply Machine Learning algorithms on the data.

  • Python For Data Visualization

Matplotlib or Plotly are among the most extensively used libraries in Python for Data Visualization operations. These libraries support Data Scientists with interactive dashboards that help in depicting the data in the form of attractive visuals. 

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