Can a Java Developer Become Data Scientist

IT jobs have been the most chosen career this decade, and now with the introduction of high-level interfaces, there has been a rush across industries to transition into Data science. Data scientist jobs have been now an icon among technical youth. IT professionals from different job profiles are switching to Data scientists. There are many programming languages, including Python and R, that can be used in Data Science. But is it possible for a Java developer to get into data science? Is Java programming language can be helpful in building Machine Learning Models? Well, the goal of this blog is to discover if a Java developer can become a Data Scientist or not. Keep reading.

Java Developer and Data Scientist Job Responsibilities

Java developers are responsible to build applications and software with Java programming language. They analyze and identify problems within the software and come up with efficient solutions. As Java is widely used in organizations, the role of Java developers may vary - one can manage a particular application or work on multiple applications simultaneously. Although Java is losing its popularity over the past few years, organizations have continued to use it because of its reliability and good support.

Major responsibilities of Java developers

Design and build applications using Java programming language.
Take part in software and architectural development activities.
Analyze, debug and maintain java applications and software.
Identify production and non-production issues in Java applications.
Recommend changes to improve the efficiency of java application processes.
Develop application code for java programs.

On the other hand, data scientists are analytic professionals responsible for collecting, analyzing and interpreting data to help organizations make important business decisions. The data scientist role combines technical jobs including mathematician, statistician and computer programmer. As a Data scientist, you have to use advanced analytics techniques to make inferences and analyze market trends, stock trades, financial risks, cybersecurity threats, and more. Data scientists typically mine data for information to predict customer behavior, detect fraudulent transactions and identify new revenue opportunities.

Major responsibilities of Data Scientists

Collect both structured and unstructured data to analyze them.
Organize data into usable formats.
Build predictive models.
Build machine learning algorithms.
Enhance the data collection process.
Process, cleanse, and verify data.
Analyze data for trends and patterns.
Set up data infrastructure.
Develop, implement and maintain databases.
Assess the quality of data and remove or clean data.
Generating information and insights from data.
Prepare reports for executive and project teams.
Create visualizations of data.

Data scientists are in every industry, including tech companies, manufacturing, government agencies, education, healthcare, and research institutions. A data scientist career is now considered more highly sought-after than a java developer career.

Importance of Java Development for Data Scientists

Data scientists are likely to use Python and R more than Java programming languages. According to a survey, only 21% of data scientists use Java which is way less than Python (83%) and SQL (44%). They use Python for its REPL capabilities and quick algorithm experimentation and R for data visualization and representation. But a data scientist must know using Java as it offers a host of other services which is very important in creating business applications. It helps in data science functionalities such as data analysis, statistical analysis, data processing, data visualization, and NLP. Below are the importance of Java for data scientists.

Easy to Understand

Developers feel more confident with Java as coding in it is very easy. Besides, it has an extensive user base which makes it one of the most sought-after skills in the market. Organizations typically use Java for quickly executable projects. Java is a legacy language that is used in applications and companies throughout the world. The simplicity of Java makes Machine Learning models in Data Science easier. Java helps even beginners in data science easily understand the data model.

Excellent Scalability

Java programming language in Data science can be used to create scalable applications as per business requirements. Java is the best option to scale up and scale out features in applications along with load balancing options. As a data scientist, you will use Java to scale Machine Learning applications with ease.

Unique Syntax

Java has a unique syntax that is very easy to understand. This syntax allows you to understand conventions, variable requirements, and coding methodology. Java is strongly typed and it is already predefined in the structure of the language. Organizations maintain a standard syntax for the code repository. It makes sure the developer code according to conventions for the production codebase.

Excellent Frameworks for Data Science

Java has excellent frameworks for data science that provide developers with the basic functionality to help them save time and money.

Some of the popular machine learning frameworks in Java are,

Deeplearning4J: An open-source, deep-learning toolkit to deploy neural nets.

ND4J: A toolkit for scientific computing, signal processing, and linear algebra.

Apache Mahout: A scalable and distributed algebra framework for classification, clustering, and recommendation.

Major frameworks in Java for data handling are,

Hadoop: A framework that uses the MapReduce algorithm to store data in a distributed file system.

Kafka: It uses a TCP-based protocol for message set abstraction to naturally group messages to form linear writes.

OLTP systems

OLTP (Online transaction processing systems) and data warehousing use mainframe systems for batch processing, and the Java programming language ties more naturally into that architecture. Java is integrable with COBOL and middleware software. It can also be combined with OLTP standards and architectures. Organizations invest in applications that perform data analysis on scalable systems with transaction processing designs for which Java is very suitable.

Wrapping Up

The bottom line is that you can become a data scientist Now you know how useful Java programming is in data science. The bottom line is that a java developer can become a data scientist. Java is a versatile object-oriented language with tons of functionality. It has excellent performance and speed and provides security capabilities, platform independence, and network-centric programming. Java is very useful to apply machine learning algorithms in data science and build real-world applications. It allows you to build adaptive and predictive models based on batch and stream processing techniques.

Having knowledge of Java is vital to become a data scientist but that’s not enough. There are many other things besides Java that you will have to learn to become a data scientist. TechAxis provides you with the best data science course in Nepal. For the past few years, we have been considered the best IT academy to offer students data science training in Nepal.