Why Java Should Be Mandatory For Data Science

java for data since coding language bootstrap business blog

Right from combining advanced statistical and quantitative skills with real-world programming ability, Data science is a pretty exciting field to work in. According to several reports technologies like data science, machine learning and artificial intelligence are attracting big money today. As a result, more and more organizations are investing millions in research and people to build powerful data-driven applications. 

In the current growing cyberspace of the 21st century, coding turns out to be the hottest trend happening across the globe. Being a programmer, I am sure you must be knowing that the way of the world by now and would be smart enough to decide which programming language best compliments and upgrades your existing skill set. Coming back to data scientists, coding is the key skill for custom analysis and data visualization. Now one of the most common questions that is asked by anyone who is beginning with a career in Data Science is – “What programming languages should I learn to get started in the field of data science?” Well, the following post has come up with a good choice of programming languages that must be considered by beginners as well as experienced geeks. 

Top 3 Languages To Learn 

1. Java 

Developed by Oracle, the programming language has evolved on the strong basis for professionals all across the globe. One of the finest benefits of using this language, in particular, is that once compiled, it can be easily used across platforms, thus eliminating the need for language dependent compilers. In addition to this, Java also tops the popularity charts on tech websites such as Mashable and ITworld. 

A major transformation is seen in the IT industry, i.e. from a software provider to providing on-demand software through software-as-a-service (SaaS) framework. As a result, Java programmers continue to be in demand even if the world shifts to SMAC (Social, Mobility, Analytics, and Cloud). 

It pays to be as proficient in Java as possible. Make sure to study hashmap java as well to expand your skillset for work with clients or companies.

2. Python 

There is no shortcut to success but if you are a quick learner and aim to get along with a widely used, easy to learn a programming language, Python is the best option to choose. Readability and compactness are some of its unique selling points which enable professionals to express same concepts in shorter code fragments. Furthermore, Python enables easier scalability due to which it is considered suitable for handling small scale and large scale applications. 

3. R Programming Language 

Python and R have long been the two languages said to have a hold on the data science world, but that’s not to say they’re the only languages worth using for data science. R is one of the most dedicated languages for statistical computing and graphics. It may quite interest you to know that R has been ranked at No. 6 in the IEEE’s Top 10 Programming Language and with the growing influence of Big Data and emergence of the Internet of Things. 

Why Choose Java Among All? 

• First, being one of the oldest languages used for enterprise development. Due to which it’s quite likely that the organizations you are working in also has a major part of their infrastructure based on Java. 

• Apart from this, most of the popular big data frameworks/tools on the likes of Spark, Flink, Hive. Spark and Hadoop are written in Java. And in the current digital world, developers can be easily divide themselves among who are working with Hadoop and Hive, rather than one who isn’t familiar with Java and the stack. 

• Next is, Java has a great number of libraries and tools for Machine learning and Data Science. Some of them being, Weka, Java-ML, MLlib, and Deeplearning4j, to solve most of your ML or data science problems. 

• With the emergence of Java 8, the entire verbosity was rectified, thus making it less painful to develop large enterprise/data science projects. Whereas on the other hand, Java 9 brings in the much-missed REPL that facilitates iterative development. 

• Java Virtual Machine known as JVM for short is considered as one of the best platforms enabling you to write code that is exact on multiple platforms. In order to create custom tools quickly, JVM is the best option to choose. Moreover, Java has a load of IDEs that improve developers’ productivity. 

• Also, don’t get confused with strong typing and static typing, strongly typing helps when working with large data applications and type safety is a feature worth having. In addition to this, the programming language even ensures professionals explicit about the types of data and variables they deal with. It makes it much easier to maintain the code base and you can safely avoid writing trivial unit tests for your applications. 

• Last but certainly not the least comes the scalability, And Java is excellent when it comes to scaling your applications. In case, if you are building larger and more complex ML / AI applications, choose none other than Java as your programming language. 

Java Justification Conclusion

If you’re a Data Scientist, a Machine Learning or Deep Learning Engineer, go ahead and try your hand at Java programming language. It's versatile and powerful for data science and I guarantee that it won’t disappoint your company. 

I hope you enjoyed this article about why java should be the mandatory coding  language for data science applications.

Interested in more articles about business technology?

Read My Related Posts:

- How Geolocation Tech Can Help Your Business

- A Basic Overview Of Personal Cloud Security

Read More Articles Below

Official Bootstrap Business Blog Newest Posts From Mike Schiemer Partners And News Outlets