In recent years, Machine Learning, Artificial Intelligence, and Data Science have become some of the most talked-about technologies. These technological advancements have enabled businesses to automate and operate at a much higher level. Companies of all sizes are investing millions of dollars in data analysis and on professionals who can build these exceptionally powerful data-driven products. Although there are many programming languages that can be used to build data science and ML products, Python and R have been the most used languages for the purpose. In recent years, quite a few organizations have preferred Java to meet their data science needs. From ERPs to web applications, Navigation Systems to Mobile Applications, Java has been facilitating advancement for more than a quarter of a century now. In this blog, we are going to explore how Java for Data Science is a great option to have. We will also discuss how Java frameworks, scalability, syntax, and processing speed can be crucial when you develop projects in data science using Java. So let us get to it.
Is Learning Java Mandatory?
If you wonder whether or not to learn a programming language, it depends on what you want to build with it. For example, if you like frontend development, learning C++ or an assembly language is not for you. Just like that, a game developer does not always pay a lot of attention to HTML and CSS. Each programming language is developed to serve a specific objective to start with.
Java has evolved over the years and today the language finds its application in fields like fintech, eCommerce, custom enterprise web applications, android apps, distributed and data science. So, the bottom line is Java is not mandatory to learn but if you learn Java you will be able to develop your desired product. Here is how -
- Lots of career opportunities: Java dominates in areas like the software industry, Enterprise services, web servers, Android apps. You get to choose the field of your choice as a Java developer.
- Java follows Object Oriented Programming(OOP): By learning Java, you won’t just know the programming language, you will be equipped with one of the best methods in all programming today.
It is recommended to take part in a data science bootcamp and get a hands-on approach to building data science projects with Java.
Importance of Java for Data Science:
When it comes to data science, Java delivers a host of data science methods such as data processing, data analysis, data visualization statistical analysis, and NLP. Java and data science allow applying machine learning algorithms to real-world business products and applications. Data Science, Artificial Intelligence, and Machine Learning are tempting big money today. So if you can program in Java, you know you have an important skill. Hone your Java skills and use them for data science with our data science training that includes dedicated tutorials on data science for Java developers as well.
Java is suited for data science due to the following features -
Data Science Frameworks Using Java:
In order to stay relevant in the space of digital transformation, we suggest "selecting the right machine learning tool". Some of the data processing frameworks that use Java do the same for you. These frameworks help you come up with precise predictive models while your infrastructure can continue having the existing technology stack.
We are listing some of the Java and data science tools that would help you to keep a suitable interface to the production stack.
- DL4J for Deep Learning
- ADAMS for Advanced Data Mining
- Java for Machine Learning Library
- Neuroph for Object-Oriented Artificial Neural Network (ANN)
- RapidMiner for machine learning workflow
- Weka for Waikato Environment for Knowledge Analysis
Java is Easy To Understand :
Java is based on object-oriented programming, as a result it stays popular among programmers. While Java cannot be as easy as Python, it is fairly beginner-friendly and easy to understand.
Java Offers Scalability to Your Data Science Applications :
Java for data science is perfect when it comes to scaling your products and applications. This makes it a wonderful choice when you’re considering building extensive and more complex ML/AI applications. If you are just starting out to create your products from the scratch, it is a good idea to choose Java as your programming language.
Unique Syntax in Data Science Using Java:
Java programmers are usually clear about the data types, variables, and data sources they deal with. It makes it easy for them to retain the code base and skip documenting trivial unit tests cases for products and applications. Java 8 included Lambda expressions, which corrected most of Java’s rambling, thus making it less distressing to develop large business/data science tasks. Java 9 gets in the much-missed REPL, which enables iterative development.
Processing Speed and Compatibility:
Java is highly functional in several data science processes like data analysis, including data import, cleaning data, deep learning, statistical analysis, Natural Language Processing (NLP), and data visualization. The majority of code in Java is experimental. Java is a language that is statically typed and compiled, whereas Python is a dynamically organized and analyzed language. This single difference gives Java a faster runtime and more comfortable debugging.
Why Java for Data Science?
Java uses Java Virtual Machine (JVM) extensively for derivatives and frameworks that affect machine learning data analysis distributed systems in enterprise settings. Not just that, there are many reasons why Java if suitable for Data Science.
- Java is a language with a huge community in words
- Java is easy for almost all programmers to split functionality
- Java is strongly typed
- Java programming permits programmers to be explicit about data and types of variables data they deal with. This is helpful in the era of data science and data management machine learning.
- The suite of mechanisms for Java is rather well developed. A range of mature elements and IDEs allow developers to be well productive.
- The Java Virtual Machine (JVM) is especially good for documenting code that looks matching on multiple platforms and it works well the big data space.
- Scala is being used in machine learning technologies and big data processing tools like Apache Spark. Scala is built on the JVM and performs rather well with Java as compared to Scala.
- Decisions connected to programming language preference are made to reduce programming, features, code training maintainability and tooling.
Where Does Java Fit In?
Java is being used in basically all the layers of web development. Web development at a very minimal level consists of a Client and a Server. Most of the famous and scalable frameworks for the Client, Server, and databases are built using Java. Java is very big in Financial Services. There are lots of global investment banks like Citigroup, Goldman Sachs, Barclays, Citigroup, Standard Charted, and other banks that use Java for reporting front and back headquarters electronic trading systems, reporting settlement and verification systems, data processing projects, and more.
What Does the Future Hold?
Data Science is disrupting businesses along with other latest technologies. The challenges that businesses dealing with data science face are selecting the right stack of technologies, onboarding the right set of developers with the right set of data science skills. Java developers can make use of data science to produce virtually any product and it's particularly well-suited for building scalable platforms.
In case you realize that the tech stack you are using has restrictions, you can expand it by making something in Java. It's more comfortable for Java developers to use technologies that need grid computing. Java for data science is becoming common not only because Java is the "best" programming language for Data Science but java developers are known to come up with visions that many data science products and applications are built upon. The knowledgehut data science bootcamp gives you the same opportunity where you work with more than 100 data samples and build data science projects around them.