The world today is generating a voluminous amount of data than ever before. IDC predicts that the worldwide data will reach 175 zettabytes by 2025. Managing such amounts of data provides enterprises the ability to deliver enhanced business services. However, it requires inclusive knowledge and proficiency in big data analytics capabilities. Python programming language offers a large number of libraries to work on big data. Thanks to its easy readability and statistical analysis capacity, Python is the most widely used in data science, AI, machine learning, and deep learning.
The field of data science relies heavily on the predictive capability of Machine Learning (ML) algorithms. Python offers an opportune playground for experimenting with these algorithms due to the readability and syntactical efficiency of the language. The vast availability of ML libraries accessible to Python users makes it an even more attractive solution to interpret the immense amount of data available today. This article explores the top 10 ML packages that you need to know, as well as the advantages and disadvantages of each. A Python framework is an interface or tool that allows developers to build ML models easily, without getting into the depth of the underlying algorithms.
Python for Data Science: Learn Data Science From Scratch This Python tutorial focuses on the basic concepts of Python for data analysis. In both cases, you are at the right place! Python is the most popular programming language for the data science process in recent years and also do not forget that data scientist has been ranked the number one job on several job search sites! With Python skills, you will encounter many businesses that use Python and its libraries for data science. Thanks to the large libraries provided, The number of companies and enterprises using Python is increasing day by day.