Python is among the five most popular programming languages in the world. It’s popularity continues to rise among the devs and there are two main reasons for it. One, Python is very easy to learn, and two, it is compatible with tools for Big Data, Artificial Intelligence, Robotics and Cyber Security systems.

Use of Python in Data Sciences: Python is one of the best programming languages for data science. It is highly compatible with machine learning and data mining languages like R, Julia and Java. It can load, transform, analyze and visualize datasets which comes in very handy when dealing with data science projects.

Scientific Computations

NumPy, a Python library that facilitates execution of scientific computations offers a host of functionalities for creating and manipulating arrays. It is also used for transforming arrays based on application. Moreover, it allows you to work with trigonometric functions and hyperbolic functions and rounding numbers as well. Since, data science involves scientific and statistical functions, NumPy has wide use case for data mining experts.

Analyzing Datasets

The Pandas library is used by almost every data scientist out there. The library provides high-performance and easy to sue tools for loading and working with datasets. You can also use Pandas for cleaning up messy data, which is a commonly performed data pre-processing task. Also, Pandas is useful for reading and writing data from diverse sources.

Visualizing Datasets

Seaborn is a Python library used for visualizing datasets. It is based on Matplotlib, the 2D plotting Python library. With Seaborn you can plot linear regression models, a very common method of data analysis. The library also provides color palettes that can be used to represent different trends and patterns.

Machine Learning Models

The SciKit learn library is an open source and simple to use Python module that can be deployed for machine learning and business intelligence. The library leverages NumPy and Matplotlib to enable data mining.

Interactive Environment

JupyterLab module provides an interactive and integrated environment for using the Python libraries listed above. It is highly flexible and can be configured and customized as per requirement by data scientists.

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