A programming language is like a tool in every programmer’s toolbox and essential to every programming activity. Choosing a programming language from popular ones is often confusing. Java and python are battling for the top position. Python is getting popular day by day and passes Java behind, but java is holding onto its place.
Programmers can do almost every task using these two programming languages. Java and Python are both object-oriented, general-purpose programming language. Python is called an interpreted programming language. But, Java can be considered both bytecode and interpreted programming language. There are slight differences between them, which makes Python better than Java for data science. Let’s see why Python is the most preferred and better programming language than Java:
Productivity: It would be more productive If a programming language can give the same output by typing less because programmers will need to spend less time writing code. In Python, you can do the same thing in one line of code, whereas Java needs ten lines of code.
Code Readability: If you look at the Python language philosophy, you will see that it was built for readability and less complexity. If you look at a Python and a Java program, you will see the difference. Anyone having basic programming language knowledge will be able to understand a python code. But in the case of Java, it’s hard to understand the code.
Easy to learn: Learning Python is much easier than learning Java. Because in Java, you have to understand the class structure, data types, and different keywords like static, final void, etc., to write a basic program. But in Python does not have many syntactical requirements compared to Java. Anyone can write code much quicker with the basics of Python language. It’s great for data scientists because they don’t have to spend time learning language syntax and writing the code.
Quick Prototypes: In Java, a program needs to be compiled first and run all at once. The entire program needs to run again if any changes were made in the program. On the other hand, Python can manipulate a single line of code without re-run the whole program. It’s a handy feature for data scientists.
Ease of libraries: Python has a vast number of powerful libraries for data science. Tensorflow, PyTorch, Scikit-Learn, Matplotlib, Numpy, Keras, Pandas, etc., are some popular machine learning libraries. We can easily import and use these libraries in Python. Java is also suitable for data science. It also has some great libraries, like Weka, MOA, Mallet, Deeplearning4j, OpenCV, Selenium, etc. But when it comes to implementation, readability, easiness, and simplicity, Python wins.
Java and Python are both excellent for data science. But data scientists usually prefer python for its code-readability and great support libraries. It’s also easy to learn and quick to write. Considering all of these points, Python is a better programming language than Java for data science.