Java vs Python for Data Science in 2021-What's your choice?

Java vs Python for Data Science in 2021-What's your choice?


Why do data scientists prefer Python over Java?

Java vs Python for Data Science- Which is better?

 Which has a better future: Python or Java in 2021?

These are the most common questions that our ProjectAdvisors get asked a lot from beginners getting started with a data science career. This blog aims to answer all questions on how Java vs Python compare for data science and which should be the programming language of your choice for doing data science in 2021.

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Java vs Python - Which language fills the need and meshes well with data science?

Java vs Python for Data Science in 2021

According to Popularity of Programming Languages (PYPL), Python and Java are two of the most popular programming languages in use as of June 2021. They are used by various enterprises and developers across the globe today. Take for example companies such as Netflix, Google, Instagram, and Spotify. Python is used heavily in the backend to process the data. Netflix and Spotify use Python to generate their recommendation engines. Instagram switched to Python as its primary programming language in 2017 and is using it ever since. Java is also used by many big companies including Uber and Airbnb to process their backend algorithms.

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Here is a poem written by Tim Peters called “The Zen of Python”, which can be read by simply typing “import this” on a Python console. It brilliantly sums up some intriguing things about Python as a programming language. Like the poem says, “if the implementation is easy to explain, it may be a good idea” to familiarise yourself with Python. Python was initially invented as a hobby project by its inventor, Guido Van Rossum, and has become one of the most popular data science programming languages in use today.

Java vs Python for Data Science

Python was made as a hobby project but Java was created by accident. Around 1992, James Gosling and his team were building a set-top box which was started by “cleaning up” C++, which resulted in the birth of a new language, which was initially called ‘Oak’, but later on. renamed to Java.

Why do data scientists love Python for Data Science?

Professionals working in the data science domain- be it data scientists, machine learning engineers, or data analysts don't want to be bogged with a programming language that has complex syntax and limited libraries for handling large volumes of data when applying complex computations and calculations. This is the major reason Python has shown itself to be a prevalent choice for Data Science. There are always new data science and machine learning libraries rolled out to meet different data science requirements.

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Python is a simple and easy-to-learn programming language. It requires much fewer lines of code than other programming languages to perform the same operations. Due to its simplicity, it is easier to keep the focus primarily on handling computations rather than getting caught up in the details of managing the program flow. Python has excellent memory management capability, especially garbage collection. This makes it an excellent programming language of choice by data scientists for managing large volumes of data. Python is a cross-platform programming language, which means that the same code works across different environments without any changes made to the code. This makes it easy to switch between environments if the need arises. 

Java for Data Science  - Should data scientists learn Java?

Is Java right for your data science projects? There is no right or wrong answer to this but knowing Java is definitely beneficial because it provides a host of other services when working with data science applications.  Many top companies like Spotify,  Uber, continue to use Java along with Python to host business-critical data science applications.

Many data scientists tend to incline to Python and R for writing programs for analysis and processing of data. However, frameworks like Apache Spark, Kafka, Hadoop, Hive, Cassandra, and Flink all run on the JVM (Java Virtual Machine) and are very important in the field of Big Data. The JVM too is cross-platform and allows the same code to be written in multiple environments. Java is a highly scalable programming language. Building highly complex functions is easy in Java since it makes it easier to scale up or scale down and provides excellent load balancing features. Java is strongly typed, which means that the data type of a variable is determined at the time of initialization and the same data type of the variable has to be maintained until the end of the program. The type can only change by explicit typecasting. 

Java vs Python for Data Science - A Comparison 

A good way to make decisions at times is to have a deeper look at the pros and cons associated with two sides of tackling a question. If you are a beginner to data science or are starting a new data science project and are confused about which language would be best suited for you, here is an in-depth look at some key points to be considered when deciding on a programming language.

Java vs Python for Data Science- Syntax

Python is a dynamically typed language, whereas Java is a strongly typed language. This means that in the case of Python, the data type of a variable is determined at runtime and can also change throughout the life of the program. In Java, a data type has to be assigned to a variable while writing the code, and this data type remains the same throughout the life of the program unless it is explicitly changed. This allows ease of usage in the case of Python when it comes to writing the program. Dynamic typing allows a program to be written with lesser lines of code. Python is valued for its simplicity and ease of use. It is well-known to be easier to learn and to use and is generally the programming language that is the first preference for novice programmers. Python also does not follow indentation rules, enclosing braces, or the need to use semicolons. Java, on the other hand, has stringent syntax rules. If the syntax rules are not followed, the code will give an error at the time of compilation and will not run. 

Java vs Python for Data Science- Performance

In terms of speed, Java is faster than Python. It takes less time to execute a source code than Python does. Python is an interpreted language, which means that the code is read line by line. This generally results in slower performance in terms of speed. The debugging too occurs only at runtime, which could also result in issues when running codes. Another point to note is that the data type of a variable has to be determined at runtime in the case of Python. This too, tends to slow down the speed of execution. Unlike Python, Java is also capable of handling multiple computations simultaneously, which also adds to its speed. 

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Java vs Python for Data Science- Frameworks and Tools

Python and Java provide a good collection of built-in libraries which can be used for data analytics, data science, and machine learning. Apache Spark is an open-source analytics engine that is used by data scientists for large-scale data processing. Apache Spark provides high-level apis in both Java and Python. These APIs have applications in big data and machine learning 

Python Data Science Libraries

Python Data Science Libraries

Let’s take a look at some of the libraries provided by Python for the purpose of data analysis and processing-

  • Python Pandas: Pandas is an open-source Python library mainly used to process large datasets by supporting loading, organization, manipulation, modeling, and analysis of data sets. It allows high-performance management of data using its powerful data structures. Pandas allow cleaning of messy datasets enabling them to be more readable and relevant. With its DataFrame object, Pandas can provide default and custom indexing. Pandas provide tools for loading data into in-memory data objects from various file formats. 

  • Python NumPy: NumPy is short for “Numerical Python”. It is a Python library that is used for working with arrays. NumPy allows a developer to perform mathematical and logical operations on an array. NumPy also supports functions for working with Fourier transforms and routines for shape manipulation. It also has built-in functions for operations related to linear algebra, matrices, and random number generation. NumPy is often used along with Matplotlib and SciPy as a replacement for MatLab.

  • Python Matplotlib: Matplotlib is an open-source Python library that is used to enhance visualization by providing graph plotting tools. Matplotlib allows the creation of 2d graphs and plots using Python scripts. The Matplotlib library also helps to make visualization more appealing in that it supports colors and color maps. It also allows the creation of animated and interactive visualizations in Python. 

  • Python SciPy: SciPy is an open-source scientific library of Python used to solve complex scientific and mathematical problems. The SciPy library is built to work on top of the NumPy extension. SciPy allows for user-friendly and effective functions for numerical integration and optimization.

  • PySpark: The Apache Spark community released a tool called PySpark specific to Python to support Apache Spark from Python. PySpark allows one to interface with Resilient Distributed Datasets (RDD’s) in Apache Spark and the Python programming language. This is done using the Py4J library, which is integrated within PySpark and allows Python to dynamically interface with JVM projects.

  • seaborn: Seaborn is a Python library based on Matplotlib for the purpose of data visualization. It provides a high-level interface for drawing attractive and informative statistical graphics. It also defines simple high-level functions for common statistical plot types and integrates with the functionality provided by Pandas DataFrames.

  • SciKit-learn: The SciKit-learn library of Python can be used for data mining and data analysis. It contains a wide range of supervised and unsupervised learning algorithms that work on a consistent Python interface. Some of the machine learning functions that Scikit-learn can handle include classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

  • PyTorch: PyTorch is an open-source tool based on the Torch library. It has a range of libraries that provide computer vision, machine learning, and natural language processing tools. It is easy to learn and to use. PyTorch can seamlessly integrate with the Python Data Science stack, including NumPy. PyTorch provides a framework to build computational graphs and change them in runtime. It also offers simplified preprocessors and custom data loaders.

  • Keras: Keras is an open-source library that is used for neural networks and machine learning. It works with neural network building blocks like layers, objectives, activation functions, and optimizers. Keras has features to work on images and text images. Keras also supports convolutional and recurrent neural networks other than the standard neural networks.

  • TensorFlow: TensorFlow is an open-source library used for machine learning. The primary use of TensorFlow is for the training and inference of deep neural networks. It is a symbolic math library based on data flow and differential programming. TensorFlow comes with a flexible and elaborate ecosystem of tools, libraries, and resources that allows developers to easily build and deploy ML-powered applications.

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           Java Data Science Libraries

            Like Python, Java also has a range of libraries for working with Data Science. Some of which are: 

Java for Data Science

  • Deeplearning4J: It is an open-source framework written for the JVM which provides a toolkit for working with deep learning algorithms. It offers comprehensive support for building, training, and deploying neural networks. Deeplearning4J is a composable framework. This means that shallow neural networks such as restricted Boltzmann machines, convolution nets, autoencoders, and recurrent nets can be integrated to create deep nets of varying types. Deeplearning4J also provides extensive visualization tools and a computation graph.

  • ND4J: ND4J is short for N-dimension array objects for Java and provides key modules for scientific computing on the JVM. It is modeled based on NumPy and core MATLAB. Like SciPy, it provides a toolkit for scientific computing. ND4J supports signal processing and linear algebra as well. ND4J is designed to run fast in production environments.

  • Apache Mahout: Apache Mahout is a distributed linear algebra framework written in Java and Scala. It is generally used to help solve problems such as clustering, classification, and item recommendation, such as that in a recommendation system. built-in machine learning algorithms provided on Apache Mahout clear the way for implementation of more complex machine learning algorithms rather than spending time on the easier ones.

  • Apache Spark: Apache Spark is an open-source data processing engine used for processing large datasets. It is built on Apache Hadoop MapReduce. The MapReduce model is extended to be used more efficiently for various other forms of computation, including interactive queries and stream processing. The main feature of Apache Spark is its in-memory cluster computing, which means that the data is kept in RAM(random access memory) instead of slower disk drives and allowed to process in parallel. Apache Spark comes with built-in modules for streaming(Spark Streaming), SQL (Spark SQL), ML (Spark MLlib), and graph processing (Spark Graphx). Spark provides built-in libraries in Java, Python, and Scala.

  • MALLET: Machine learning for Language Toolkit is an extensive open-source library that contains algorithms and utilities for Natural Language Processing. MALLET has a command-line interface. It includes Java API for naïve Bayes, decision trees, maximum-entropy, and hidden Markov models, latent Dirichlet topic models, conditional random fields.

  • Java-ML: Java Machine Learning library provides a vast collection of machine learning and data mining algorithms, it contains algorithms that can be used for data preprocessing, feature selection, classification and clustering. It is straightforward and hence easy to understand. It does not come with a GUI (graphical user interface), but algorithms of the same type have a common interface.

  • Weka: Weka is short for Waikato Environment for Knowledge Analysis as it was developed at the University of Waikato, New Zealand. Weka is an open-source machine learning library for Java. The primary uses of Weka are for data mining, data analysis, and predictive modeling. The library provides a well-developed GUI, command-line interface, and Java API. 

  • Tablesaw: Tablesaw is Java’s library for data frames and visualization. It also comes with utilities for loading, transforming, summarizing, and filtering data. Tablesaw also supports descriptive statistics.

Java vs Python for Data Science

Purpose

Python Library

Java Library

Data frames

Pandas

Tablesaw

Designed as a replacement for MATLAB

NumPy, SciPy

ND4J

Deep Learning

Keras, PyTorch

Deeplearning4J

Machine Learning

SciKit, TensorFlow

Java ML, MALLET, Weka, Apache Mahout

Apache Spark modules

PySpark

Spark MLlib, Kafka, Hive

Linear Algebra

Pandas

Apache Mahout

Data Visualization

Matplotlib, Seaborn

Tablesaw

Java vs Python - Machine Learning

Java and Python both come with a wide range of built-in libraries and tools that can be used for the application of machine learning techniques, which means that both of these languages are an excellent choice for machine learning. However, it is notable to consider that since Python is syntactically much more effortless, it is the preferred choice of programming because the learning curve for Python is comparatively less steep compared to Java, it is easier for someone who does not have any experience in either of the languages to focus more on the machine learning aspect of the algorithm than worrying about the code writability. 

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Java vs Python for Data Science - Popularity

Referring to PYPL once more, we can see that Python and Java can be considered the two most popular programming languages as of June 2021. However, something to be considered is that over the last few years, Python has grown the most - by 17.3%, while Java has reduced in popularity by 7.1%. Python has shown emerging popularity among data scientists and the field of machine learning. It is also increasingly more popular among software professionals who are new to the world of programming due to its simplicity and ease of use.

Java vs Python for Data Science - Salary

Below is the average data scientist salary in the US and India, taken from Payscale

Average Data Scientist Salary in US

Java vs Python - Salary

Average Data Scientist Salary in India

Java and Python Data Scientist Salary

In the US, the average salary is $96,609 per annum, and in India, it is Rs. 822,722 per annum. A closer look at the information provided by Payscale reveals some more details on the salaries for data scientists. In both the United States and India, we can see that a data scientist who possesses Python skills can land a job that pays higher than the market average. This is $97,889 per annum in the US, and in India, it is Rs. 833,138 per annum. This shows that Python seems to have the upper hand when it comes to salaries in the field of Data Science. 

Java vs Python for Data Science - Interviews

Attending interviews tends to be a very stressful affair. If you are attending an interview for the role of a data scientist in Java or Python, here are a few questions that we recommend you be prepared for.  Naturally, since the interview is for the role of a data scientist, be prepared for a few questions that are not programming language-specific. 

Java Questions for Data Science Interviews

Specifically, for the role of a data scientist in Java, be prepared to answer some of the questions below:

  1. Explain the concept of constructor overloading.

  2. How is Java a platform-independent language?

  3. Explain the JIT compiler.

  4. Differentiate between method overloading and method overriding.

  5. Explain the use of the ‘final’ keyword.

  6. What is the main objective of garbage collection?

  7. Can static methods be overridden/overloaded?

  8. What is the purpose of the ‘this’ keyword in Java?

  9. What is aggregation?

  10. How does an exception propagate in the code?

Python Interview Questions for Data Science 

  1. Differentiate between lists and tuples.

  2. What is a negative index and how is it used in Python?

  3. What are lambda functions?

  4. Explain how list comprehensions work in Python.

  5. How can you copy objects in Python?

  6. Explain the use of decorators in Python.

  7. What is the difference between pass, continue and break in Python?

  8. Explain map, reduce and filter function in Python.

  9. Differentiate between mutable and immutable objects in Python

  10. What is the difference between “is” and “=”?

  11. What are the different parts of a plot in Matplotlib?

  12. How do you plot a histogram in Matplotlib?

  13. What is broadcasting in NumPy?

  14. Can we create a DataFrame with multiple data types in Python? If yes, how?

  15. How do you find unique values in a DataFrame?

  16. How do you plot a line chart/bar graph on Seaborn?

  17. Write a code to sort a DataFrame in ascending/descending order.

  18. What is the standard data missing marker used in Pandas?

Java vs Python for Data Science - What’s your choice?

Java vs Python for Data Science

Thorough research on the use of Python in Data Science vs. the application of Java in Data Science shows that both of these programming languages have their benefits. It ultimately comes down to picking a programming language based on a particular individual or organization’s comfort and requirements. If an organization has all its code built in Java already, it is more convenient to add the Data Science bits in Java. Similarly, Python may be preferred by someone who is a beginner to the field of Data Science, since it allows a user to concentrate on the Data Science aspect and worry less about the actual program flow. Due to its ease of use, Python has gained more popularity in the field of Data Science. To better understand using Data Science with either of these programming languages, it is essential to have some hands-on experience. Experiment with writing simple and then later on more complex Data Science codes in Python and Java to better understand which of the two you find more comfortable.

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