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 TOP 6 REASONS DATA SCIENTISTS SHOULD KNOW JAVA

The profession of Data Science is fast leapfrogging, leaving all other disciplines behind. The increase in the popularity of data scientists is because of their significant role in using vast amounts of information to generate value and utilize them as an asset for industries. However, apart from their educational degrees, data scientists are expected to have prerequisite programming languages, and Java tops the list of the best programming languages.

There are ample reasons why data scientists should consider learning Java programming. Let us discuss the top 6 reasons why:

1. Java programs are platform-independent and portable. 

Java programming language can be written on multiple platforms and runs on most operating systems such as Windows, Mac OS, and Linux. Most importantly, Java is a software-based platform, and the compiler can compile the Java code and convert it into byte code.

2. Java has an excellent suite of frameworks for data science.

Java has many libraries and tools, such as a range of IDEs and well-developed features that help the developers in every phase. Weka is probably one of the famous and commonly used data mining tools in Java. Java has powerful developer tools to create and test Java programs.

3. Java is more efficient.

Java is considered one of the fastest and most efficient programming languages compared to other languages like Python. Since Java’s allocator is tuned to work with short-lived allocations, business applications get spurred when coders use Java.

4. Java syntax is intended for novice developers.

Java is a strongly typed programming language as it restricts intermixing of variables with some different data types. Java syntax has codes that belong to classes, and the values are objects. It is unique and allows developers to comprehend conventions and understand coding techniques. Java syntax is easy to understand and hence, accepted across the globe.

5. Java can integrate well with the latest IT standards.

IT leaders have already recognized the potential of Java to link with the modern generations and are making use of Java’s ability to combine with newer standards. Java can also integrate with COBOL and middleware software. It can naturally connect to OLTP as well.

6. Java is scalable.

Java is considered scalable as it can change the hardware configuration and run on the application server as needed. A data scientist will find creating and scaling complex applications to be easier with Java.

Conclusion

Java has stood the test of time, so it is being called the pillar of the programming stack. Applying Java in your data science projects can skyrocket your data science career to the next level. 

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