Python is quickly becoming the language of choice for Data Analysts, and in the next five years the “anaconda’ of programming languages will be the hub in the world of data science. Python is open source and is object-oriented, as it adapts to a wide range of libraries, making it easier for engineers to perform actions.
Python, a language introduced back in 1990, became popular for its use by data scientists. By 2020 it became the fourth most widely used language by developers in the field.
The three most utilized areas by Python for data science are Data Mining, Data Processing & Modeling, and Data Visualization.
Data Mining: The most important and vital part of data science is data mining; can be defined as the process of extracting useful resources and information from available databases. Scarpy and BeautifulSoup are two widely used libraries.
Data Processing and Modeling: Data processing refers to the process of collecting, converting, and segmenting data while modeling refers to the process of creating data models using syntax and Python system language. NumPy and Panda libraries are used in this category.
Data Visualization: Matplotlib and Seaborn in Python help to convert long lists of numbers into easily recognizable images, histograms, pie charts, temperature maps, etc.
There are many benefits of Python as it is easy to learn, it can be used in many fields, and its working speed, etc.
Cloud Computing, Machine Learning, and Big Data are some of the hottest trends in the world of technology, helping many organizations transform and improve their processes and workflow where Python is frequently used.