Java developer and data scientist are two different positions and work in different fields. Although both carry out their work on computers, the tools, processes, and methodologies used for their respective works differ, and they derive different results for their clients. But the similarity between the two is that they are in great demand in the current scenario. There has been a considerable increase in the number of jobs in these fields in the last few years. Java developer and data scientist are the two top-rated jobs today. Though these two are entirely different fields, java developer sometimes has a role to play in data science. Some companies prefer java as the core programming language for data science. There are certain situations where java is preferred over other programming languages in data science. Before moving further, let us first understand what a java developer and a data scientist exactly do and their respective roles.

Java Developer

A java developer designs, develop, and manages applications based on the java programming language. They collaborate with other software engineers, web developers, and others to use java to build business applications, websites, etc. Since java is a widely used programming language, especially by large organizations, their roles can differ from company to company. They may be working on single or multiple applications simultaneously. They are continuously involved in a product or service’s entire product development life cycle. A java developer is most likely to be a part of the IT team of their organization. The job responsibilities of a java developer vary as per the experience held by the person.

These responsibilities are classified according to their experiences, like entry-level, middle-level, and senior-level developers. The responsibilities for each of these designations are different. Let us look at the responsibilities at each level.

Entry-level developer:

If you have just finished a Java programming internship or graduated in programming, you may be offered an entry-level position in an organization. You might get on-the-job training in some organizations, but most expect you to have undergone some training before hiring you. You can start as a Junior developer as a beginning because your practical knowledge of java would be less than desired at this point. But it would be enough to get you a foothold in the industry. You can subsequently climb your way up the career ladder. Entry-level jobs can also have titles like Associate Java Programmer or Assistant Java Developer. At the start of your career, you may be responsible for focusing on program architecture or be asked to create multimedia applications using authoring tools. You will support users by developing documentation and assistance tools.

Middle-level developer:

When you have worked for some years and have gained experience as a java developer, more responsibilities are added to your profile. You may be given one or more of the following responsibilities:

• Creating user information solutions through the development and maintenance of internet/intranet applications
• Analyzing user requirements and defining site objectives
• Designing database architecture and server scripting for integrating applications
• Studying and developing connectivity with network systems, information servers, and search engines
• Completing the development of applications through contributions to team meetings and managing problems of development and production
• Accepting ownership for fulfilling different requests

Senior-level developer:

As a senior-level java developer, you will handle the entire project, including ensuring a smooth architecture and managing the team. You may also be asked to liaise with other project stakeholders and beta testers. You may also have to perform some of the above-mentioned tasks, but your ability to lead the project is the most important. At the senior level, your organization may expect you to be trained/experienced in other skills, such as software architecture, software design, java beans, java servlets, network design and implementation, presenting technical information, project management, developing the budget, and web programming. All these are necessary skills for a leadership role as a java developer.

Roles and responsibilities of a java developer

Although roles and responsibilities of java developers may vary from company to company depending on their requirements, some of the general responsibilities of a java developer are listed below:

• Designing, implementing, and maintaining java application phases
• Designing, implementing, and maintaining high-volume, low-latency java applications for mission-critical systems
• Participating and contributing in all phases of the development cycle
• Write efficient and testable code
• Participate in software and architectural activities
• Check for and identify production and non-production issues
• Creating technical designs for developing applications
• Software analysis, programming, testing, and debugging
• Managing the development of java and java EE applications
• Making sure that the designs are to specifications
• Suggesting changes for the improvement of established java application processes
• Changing requirements to stipulations
• Development, implementation, testing, and maintenance of application software
• Support continuous improvement
• Ensure high availability and performance

Data Scientist

A data scientist is an analytics professional who gathers, analyzes, and interprets data to help the organization make decisions. A data scientist understands and explains the phenomena around the data to improve the organization’s decision-making process. An analytical data expert, a data scientist possesses skills to solve complicated problems. In simple language, the data scientist’s job is to analyze data and provide actionable insights. Generally speaking, a data scientist can get extra meaning from and interpret the data as a human who takes help from the tools and methods of statistics and machine learning. Data scientists must spend a lot of time collecting and cleaning the data.

The role of a data scientist is a mixture of many traditional and technical jobs. They are a mathematician, a scientist, a statistician, and a computer programmer, all rolled into one. They make use of advanced analytical techniques like predictive modeling and apply scientific principles at the same time. The whole process adopted by data scientists requires persistence, knowledge of statistics, and software engineering skills. These are essential to understand the biases in data and debugging the output from code. Working as a data scientist is a challenging job but analytically satisfying. You can be at the forefront of new technological advances.

Over the last decade, the demand for data scientists has increased phenomenally as big data has played a crucial role in how organizations make their decisions. The best thing about data scientists is that they are a part of IT and the business world. The sudden popularity of data scientists and the way they are being sought shows the thinking of the business world about big data. Data has become a virtual treasure for boosting revenue, and this unstructured information must be addressed. So, companies need someone to dig into this mass of information and take out business insights that would drive decision-making, propelling the business to new heights. That shows the importance of a data scientist in an organization.

What does a Data Scientist do?

A data scientist finds out the questions their team would ask and then try to figure out answers to those questions using data. They theorize and forecast by developing predictive models. Data scientists often work with a large amount of data for developing and testing hypotheses and deriving conclusions. They then analyze customer and market trends, cybersecurity threats, financial risks, equipment maintenance needs, etc. Data scientists help businesses by mining data and taking out such information that can be used to forecast customer behavior and find out new opportunities for generating revenue. They commonly take out information that helps in designing more effective marketing campaigns, better customer service, and better supply chain management. They also help in improving decision-making and overall business strategies.

Role and responsibilities of data scientists

The role and responsibilities of data scientists can only be fitted into a partial job description. However, there are certain activities that they routinely carry out and which can be termed as their primary responsibilities. Let’s have a look at them.

• Collecting and preparing data relevant to use in analytical applications by transforming it into a more usable format
• Improving the quality of data with the use of machine-learning techniques
• Using different analytical tools to identify patterns and trends and the relationship between different sets of data to bring out insights
• Creating dashboards, data visualization, and reports to communicate their findings to other teams, top management, and stakeholders
• Forecasting results by creating algorithms and data models
• Using data-driven techniques for solving business-related problems
• Working with different programming languages like Pythion, R, SAS, and SQL in analyzing data
• Always staying up-to-date with analytical skills like deep learning, machine learning, and text analytics, as well as the latest innovations in data science
• Collaborating with both IT and business

In specific organizations, data scientists define and promote the best practices for collecting, preparing, and analyzing data.

Who can become a data scientist?

The minimum requirement for a data scientist job in most organizations is a bachelor’s degree in a technical field. However, an advanced degree in data science, mathematics, computer science, management information systems, or statistics is always desirable. Knowledge of maths or statistics is essential to become a successful data scientist. It is said that data scientists are highly educated. Besides educational qualifications, some other unique characteristics are also necessary to become a data scientist. A natural curiosity about data and the ability to think creatively and critically are prominent among them. Another characteristic needed to become a data scientist is an interest in data collection and analysis. You need technical skills like data mining, machine learning, deep learning, predictive modeling, and data preparation and processing.

Some other skills data scientists are expected to possess are:

• Should be an expert in every stage of data science, from data collection to data cleaning to model selection, validation, and deployment
• Should have experience in the use of statistical methods for solving analytical problems
• Should know data science and machine learning techniques like K-nearest neighbors, decision trees, naive Baise classifiers, support vector machines, and random forests
• Should know data warehouse and data lake structures
• Should be able to find out new opportunities to improve the effectiveness and efficiency of business processes by applying data mining and machine learning tools
• Should be able to accumulate data from dissimilar sources and make it ready for analysis
• Should have experience with public cloud platforms and services

So, in conclusion, both Java developers and Data scientists are significantly in demand, and the demand is bound to increase with more and more companies moving towards automation and realizing the value of confidential data. While java developers need to learn the programming language and gain proficiency in it, they can get job opportunities in many industries and work in many fields. They can even work as freelancers. On the other hand, data scientists have highly specialized work, and most of their job opportunities would be in large or mid-sized organizations that generate massive data. But in essence, both are specialized fields in their own right and require excellent understanding and dedication to be successful.