Python is the most widely used programming language in Data Science

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.

It offers a better ease of access, time efficiency, improved outcomes, significant benefits, and involvement, helping in meeting the goal of the project within time and without blockades.

Managing Big Data with Python

Python is already quite prevalent in the data world. It has a large number of libraries and frameworks that can be used by developers. Many Python libraries are specifically useful for data analytics and machine learning. These libraries provide countless support for handling big data, making Python a most liked language for big data.

There are many reasons Python in big data can help and benefit developers.

Scalability

When it comes to scalability, Python comes first in the best programming languages. It has the potential to quickly boost the processing speed of data whenever the count of data is increased. Unlike other programming languages like Java and R that are unable to handle a large volume of data, Python handles an enormous amount of data very smoothly and easily.

Simple Coding

In contrast to other programming languages, Python involves fewer lines of code. It can execute programs in the least lines of code and offer assistance to identify and associate data types automatically. This language can process lengthy and complex tasks within a matter of time.

Ease of Learning

Python can quickly be learned as a non-programmer can skim the syntax of this language. There is no need to learn or understand the Python language for a programmer or developer. The support for this programming language on time from the large community helps in solving numerous live issues. Anyone can quickly learn Python by using it in real-world applications.

Multiple Libraries Support

As Python has extensive support for libraries, many libraries are apt for data analytics, visualization, numerical computing, and machine learning. Big Data requires scientific computing and data analysis, and the unification of Python with big data makes them great companions. Pandas, Numpy, Scikit-Learn, and SciPy, among others, are some Python libraries.

Moreover, Python's high speed for data processing makes it optimal for usage with big data. The data codes written in Python can be executed in a fraction of time in contrary to other programming languages. Previously, Python programming language was considered to be a slower language as compared to Java or Scala. But the advent of Anaconda has consistently made each version of Python faster than ever as well as makes it one of the most popular selections for big data in the tech industry. Python's diverse range of open-source libraries makes everything possible that a data scientist does on an average day.