Java Vs Python: Which language meshes well with Data Science?
Two of the most popular and in-demand programming languages of all time are Java and Python. Various businesses and developers use them all over the world.
Two of the most popular and in-demand programming languages of all time are Java and Python. Various businesses and developers use them all over the world. While Python is heavily used in the backend by companies like Google, Netflix, Instagram, and others to process data. Java is used by firms such as Uber, Airbnb, and others for their backend processes.
Python, which was created in 1991 by Guido van Rossum, has a very object-oriented approach that aids programmers in writing both small and big size code. Another object-oriented programming language is Java, created in 1995 by James Gosling. Although Java offers low-level features akin to C and C++, it is primarily a high-level language used for client-server web applications.
While Python has always been one of the most popular programming languages, according to the TIOBE index for October 2021, it recently overtook Java to become the most popular programming language for the first time in more than 20 years.
As a result, we’ll compare the two programming languages from the standpoint of data science.
Java Vs Python: Syntax
One of the most significant distinctions between Java and Python is their syntax. When writing code in Java, a programmer must specify the data type. And this data type cannot be modified expressly; it remains the same throughout the program’s lifespan. As a result of this feature, Java is a strongly typed language.
In Python, the data type of a variable is automatically determined at runtime. It can also be updated at any time during the program’s life cycle, making it a dynamically typed programming language.
Dynamic typing not only makes it easier to use, but it also means fewer lines of code. Furthermore, Java has very stringent syntax requirements; omitting a semicolon here or forgetting to include enclosing brackets will result in a compilation problem. On the other hand, Python does not adhere to such complicated programming structures, and as a result, it wins the syntax game since it is easier to learn and use.
When it comes to performance, Java is faster than Python when executing source code. This is due to the fact that Python is an interpreted language, meaning it is read line by line. Python is slower than Java in terms of performance because of this feature. Debugging takes place throughout the execution of a Python programme. On the other hand, Java can carry out numerous calculations at once.
Frameworks and Tools
Data science, data analytics, and machine learning tasks are supported by Python and Java libraries. Python, for example, has the following libraries: —
Pandas: It is the most widely used open-source Python library. The library is used to process huge datasets and provides data structures that are versatile, fast, and expressive.
SciPy, or Scientific Python: is a programming language that aids in solving issues in science, difficult mathematics, and engineering.
NumPy, or Numerical Python: is a key tool for statistical and mathematical calculations.
TensorFlow: It allows for the deployment of machine learning applications.
The following data science tools are available in Java:
WEKA 3: Waikato Environment for Knowledge Analysis is open-source software that provides data implementation and processing capabilities for predictive modelling, data mining, and analysis.
Apache Spark: is a user-friendly and fast big data processing engine. Based on Apache Hadoop MapReduce, Apache Spark is mainly used to process huge datasets.
Java ML or Java Machine Learning: is a library of machine learning and data mining methods that may be used to classify, process, and cluster data.
Deeplearning4j: is an open-source package that makes it easier for Java programmers to construct machine learning applications.
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