The Data Science field is a rapidly evolving domain with advanced analytical and high-end technological practices involved in building data models and sets that are relevant to market changes and helpful in developing strategies and business decisions of larger companies and corporate firms. Data science methodologies use multiple analytical software, tools, and frameworks necessary to conduct data practices from collection, and evaluation to visualization and consist of numerous domain information and conceptual learning of Statistics, mathematics, and information technology along with some key programming languages like Python, scala, R, and SAS. Such programming languages aid the data experts and scientists in data storage along with accessing and extracting information from databases, creating and simulating models based on real-world problems and learning, and also for data visualization and data reporting. In this article, we will highlight the importance of Java for data science learning and further analyze it to know about its essential features, advantages, and disadvantages in this domain along with its top applications.
Apart from Python and R programming, Java as a programming language in data science is also gaining required momentum for its speed and application creating the need to learn it for data science practices.
Also Read,
- Can Data Scientists Work From Home
- Can Data Scientist Become Software Engineer
- Can Data Scientists do Freelance
- R for Data Science
A Brief Overview of Java as a Programming Language
Java is an object-oriented programming language with a class-based module having a general purpose to enable coders and programmers with WORA facility and has compiled code that is supported by almost all digital platforms and does not need any complex recompilation.
It has syntax compiled to byte code and is almost as similar to other programming languages like C++ etc. It is considered to be one of the most popular programming languages across the globe developed by James Gosling and started its implementation in 1995.
It has a very robust and portable structure with easy to interpret and dynamic with high-performance execution. Java programming software is flexible and can run on any device from normal personal computers to supercomputers, data centers, game consoles, and others.
There are multiple versions of Java programming language and Java SE 22 launched on March 2024 is the latest and updated one. Java has specific syntaxes where its syntax codes are compiled in classes, Boolean integer values, characters, and gloating point numbers including some special classes like the java applets, java servelet technology for HTML pages, Swing application, java server pages, Java FX application and more.
Some of the top Java programming editions are the Java Standard edition, the Java Enterprise Edition for web applications and software, and Java FX for internet applications. Java for data science practices and learning is also widely used because of its robust OOP structuring and can be run on around 3 billion devices with no hindrance.
Must Read,
Important Java Tools and Framework Used in Data Science Learning
Java programming language offers many analytical and machine learning tools and high technology frameworks facilitating data science learning workflow and data modelling and structuring to enable data science practices and methodologies and programming efficient models.
Firstly, Weka is an important Java tool containing a repository of high-end machine learning algorithms for performing tasks like text mining and data mining acting as a user interface for conducting data evaluation, gathering, clustering, regression, and also essential classification.
It is open-source software with a cloud-based platform enabling data storage, processing, and management. Secondly, there is Apache Mahout, a tool by the Apache project along with other tools launched by the platform like Apache Hadoop and Apache Spark.
Apache Mahout is exclusively for Java programming language that focuses on the algorithms of machine learning and implements such algorithm structures in performing data operations like classification, recommender systems, clustering, etc with more emphasis on the scalable performance of machine learning models and applications.
It includes a linear algebra framework using Scala useful for just not data scientists and data experts but also for qualified and practising mathematicians, and statisticians with their algorithm structures and also supporting distributed backend in data science learning.
Thirdly, there is Rapid Miner which is highly useful in the data science domain devising critical data-based solutions with machine learning algorithm structuring performing data processing, manipulation, modelling, and even for automation purposes.
It has key features like model simulators, decision trees, and other essential features for data practices. Fourthly, there is deep learning for Java or DL4J which is a deep learning-oriented Java library integrating Apache Spark and Hadoop and aids in neural networking in data science practices.
Thus, java for data science learning usages has top tools and framework structures and can easily find its way into data science and analytics domain.
Explore Now,
- Data Science Course Syllabus
- Data Science Courses For Beginners
- Data Science Courses After Graduation
- Are Data Science Certificates Worth It
- Are Data Science Jobs Safe From AI
Highlighting Some Advantages of Using Java for Data Science Practices
Java as a programming language finds its usage in critical data science practices and methodologies and is widely used by data scientists and data experts while dealing with big data structures due to its scalability, portability, and also its object-oriented programming nature aiding to meet data analytical and data science needs and requirements.
It is useful in a wide range of data practices like in data processing and high-end evaluation, data modelling and structuring, and data visualization with statistics, and its application allows algorithms usage in real-life data science problems like manufacturing of top business products and services.
Java for data science programming is preferred by most data experts and professionals due to some advantages and features like it can be easily run on multiple computing devices and software environments without much of a hassle thereby enabling to devise better data solutions.
Java for data science learning has its JVM or Java Virtual Machine which is an advanced Java ecosystem that offers an independent platform to run any applications on any device with JVM compatibility and thus aids in data manipulation processes of critical data models with great ease.
Apart from this, java as a programming language is highly typed to split high-end functionality along with a huge community in words and syntaxes. Data experts can work with Java explicitly on particular data variables and types with machine learning practices.
It is also very efficient in formulating codes for multiple platforms. It has high portability and can be easily integrated with data frameworks like Apache Hadoop and Spark for devising independent solutions in dynamic data science environments.
Thus, java for data science practices offers multiple advantages due to its key features that as being architecturally neutral, simple, highly secure and trusted, multi-threaded offering high performance irrespective of the environment, and can easily be interpreted and distributed.
Also Read
- Data Science and Machine Learning
- Data Science from Scratch
- Data Science and Business Analytics
- Data Science Companies
Java Vs Python: A Detailed Comparison of the Two in the Data Science Domain
Both Python and Java for data science practices as high-end programming languages have their respective usages and advantages aiding in conducting and dealing with big data structures and are deployed in practices of data pre-processing, data manipulation, data modelling, data evaluation, and building data models through their algorithms and in-built syntaxes.
They are two of the most popular programming languages and also the most trusted having their respective strengths and suitability. While Java for data science learning supports some of the key functionalities and programming concepts, Python on the other hand has procedural and also functional paradigms that can be compiled with only the Linux software platform.
While Java is more flexible as it has a general purpose that can be compiled and run anywhere Python is not a general-purpose programming language and it is a code-readable and short syntax language with limited purposes.
Python as a programming language is slightly more used and popular for data science and analytical methodologies for its library tools and easy readability whereas Java is more popular with enterprise applications and for website and application development used by software engineers and developers.
Java as a programming language is easier to write with additional codes and concepts and easier to compile and debug whereas Python is easier to learn with some of its expressive syntaxes and structures.
Java can be used for mobile applications and website development whereas Python does not support any mobile software platform and hence is not applicable for mobile application programming.
In the data science domain, java is mostly used in fraud detection and ensuring security and network protection against any form of cyber hacking and fraud, whereas Python is used in data computation and scientific data research practices in big data analysis and evaluation, AI-enabled programming of data structures and also widely used in machine learning and regression techniques with its wide range in features like lists, strings, arrays, tuples, dictionaries, and functions.
Java language runs on software applications, and Python programming language depends on the implementation of other programming languages and their run speed.
So while Java for data science programming on the other hand is high performance oriented with secure data coding and has multiple frameworks most popular being Helidon by Oracle for cloud-based applications, Python programming language on the other hand is known for its simplicity and readable syntax structures with some power data science libraries and tools that is used in machine learning, developing language models etc.
Read,
- Psychology and Data Science
- Blockchain Data Science
- Data Science Programming
- Behavioral Data Science
- Data Science Technologies
Some of the Top Resources in Java for Data Science Learning
Java programming language is a highly flexible and compiled programming and coding language widely used in the development of numerous applications along with data science practices with syntax structure that can easily be run on any software device with a flexible environment.
Most of the learning resources consisting of Java programming language modules focus on software engineering, application, and web development although some of the usages find its way into data science methodologies as well for data manipulation and data evaluation processes.
There are top resources available in online mode that have compiled and comprehensive Java language learning curricula aiding learners to learn about the language practices along with its application in real-world case studies and data learning process.
Some of the top Java for data science learning resources are as follows:
The Software Guild: The Software Guild is a top Java programming and coding practices platform with boot camp training modules helping one to learn about basic developing tools and practices. It has sessions on object-oriented programming like the Java language syntax, debugging methodologies using Java, web development frameworks, and jQuery library access.
Codecademy: Codecademy is another Java programming language platform with its ‘learn java’ course offering basic learning lessons and Java language and teaching some essential Java language functions with free-of-cost enrolment and session.
Coursera: Coursera is one of the largest open-source learning platforms with certification courses accessed globally through online modes. It offers a Java programming fundamentals course by Duke University with specialized modules within a reasonable fee covering all Java-oriented functions and its basic features and applications in data science and analytics learning.
EDX: EDX is another very popular and quality learning platform with diverse course programs including the ‘Java for Data Science’ course having a basic, intermediate, and advanced programming curriculum with knowledge of Java language structures and its high-end functioning. It offers globally recognized certification along with eminent international faculty mentorship throughout the program.
Read Now,
- Who Can Do Data Science Course
- Are Online Data Science Courses Worth it
- Can Data Scientists Work as Data Analysts
- Who Can Do A Data Science Course
- AI and Data Science
- Data Analyst to Data Scientist
List of Some Data Science Libraries Based on Java Programming
Java programming language has libraries for conducting various tasks and practices in data science learning and domain and consists of some data analytical tools and software that aid in data science methodologies.
Since Java is an object-oriented programming language like Python, it has widespread usages in data science and analytics and such in-built libraries are an added resource that helps with data science practices.
Some of the top libraries that are used for data science learning in Java programming are:
Deep Learning 4j: DLJ4 or Deep Learning 4j is a deep learning and open source library with Java language operation and helpful in developing machine learning models and data structures with distributed computation techniques. It has integration with popular java based libraries and analytical tools like Apache Spark, Hadoop, and Tensor Flow performing some relevant tasks like image classification, recommender systems, and Natural language processing techniques to develop software structures and apply machine learning models in real-world problems of data science and analytics.
Encog: Encog is also another open-source machine learning-based data science library that is used with Java programming language along with C++ language as well. It supports advanced neural networking techniques with an intuitive API model for conducting data evaluation and data pre-processing techniques having a variety of algorithms. It supports algorithms of Bayesian networks and all other machine learning algorithms for data practices.
Java ML: Java ML or Java machine learning has multiple ML algorithms with open source features having high-end evaluation tools for conducting practices like cross-validation, and multi-threading, and can support a variety of machine learning applications. It is simple in its syntax and useful for advanced data analytical practices. It is a general-purpose library that has proper data documentation conducting feature selection, data pre-processing, and classification as well.
Rapid Miner: Rapid Miner is also a popular data science library with Java language syntax including a graphical user interface for testing ML data models and automating complex operational tasks. It is mostly used in data modelling, data evaluation, and data visualization. Formerly known as YALE or Yet Another Learning Environment, it includes extensive processes in data loading and transformation, data evaluation and visualization techniques, and also statistical analysis.
Neuroph: Neuroph is an object-oriented artificial intelligence-based library written with Java programming language creating high-end data neural networks consisting of a Java library and graphical user interface module. It has multi-purpose usages like neural layering, transfer functions, input functions, and learning rules. It is a lightweight Java programming framework with neural networking building and developing data architecture likewise.
Weka: Weka or Waikato environment for knowledge analysis is a top data science library with Java programming syntax structure having advanced machine learning algorithms for conducting practices like data mining, data clustering, classification, regression, and data visualization techniques. It is highly suitable for beginners in the machine learning domain as it does not require much ML coding knowledge and has a graphical user interface for conducting its programming.
Some of the Key Disadvantages of Java Programming in Data Science Practices
Java for data science programming has multiple advantages and top features making it one of the sought-after programming languages, however, it has numerous disadvantages. It takes longer and larger periods to run making data science practices a bit arduous to conduct along with high memory consumption.
It even has high costs for its installation and hardware operations. It has a very low level of support for other data science practices and hence its application in the data science domain is quite limited compared to Python programming language.
It also has no additional features to delete or remove garbage and has zero control over it making it less flexible compared to other programming languages.
Even Java programming lacks advanced interactive features making it less interactive with machines and cannot be run quickly and efficiently on any machine as it misses some of the explicit pointers.
Java programming language also does not have a very strong backup facility and lacks a proper storage mechanism as well.
Why Java is a Key Programming Language for Data Scientists?
Apart from Python programming and Scala and R programming language, Java programming language has some of the top usages in data science practices however it is limited.
Some of the popular data analytical and data science practices where Java language is used are in data exporting and data importing techniques, in data cleaning and removal of unwanted data structures in its pre-processing phase.
It also is deployed in statistical analytics practices and its related learning in probability, random variables, and more, in machine learning and data classification techniques, in deep learning practices with CNN and KNN data modelling and neural networking techniques.
It has text analytics modules with Natural language processing techniques along with usages in conducting data visualization and creation of effective data dashboards.
Java is a highly scalable programming language and such a feature is used by data scientists and applies Java libraries for connecting various databases crucial for data accessibility and data manipulation in various data-based problems.
Its usage can be seen in web application development based on resilient and reliable data structures with the execution of parallel and concurrent levels of data science projects and learning outcomes.
Faqs:
1. How to learn Java for data science methodology?
Ans: One can easily learn Java for data science by learning the fundamentals from various online Java programming courses and then practising coding methods for data science learning. One can opt for Java programming-based data analytics courses as well.
2. Does the data science course cover Java programming modules?
Ans: Yes, some of the data science and analytics courses offer modules in Java learning however it is recommended that one learn Java programming through its specialized courses available on multiple platforms.
3. What are the domains in data science that require Java programming?
Ans: The domains of data science practices that require the usage of Java programming language include data manipulation with file manipulation, database integration, string manipulation, concurrent programming along with machine learning practices, and data visualization techniques with charts, graphs, and many others.
4. Is learning Java programming for data science practice a good option?
Ans: Yes, learning basics and advanced Java programming modules aid in data science and analytical practices mainly in the processes of big data manipulation, applying data science libraries with Java syntaxes in machine learning and regression practices.
Conclusion:
Thus, the Java programming language is essentially one of the popular tools with data scientists and data experts using it and its syntaxes in real-world applications of data science processes from data classification, regression, and data clustering to even big data visualization techniques.
Due to its high scalability and portability, the source codes of Java can be operated in any platform with just JVM installation along with aiding in building technologically resilient and robust machine learning-based data models and structures with advanced features and operations.
Thus, the Java programming language is indeed a useful and data science-friendly language apart from Python, Scala, and R.
