<img style="display: block; margin-left: auto; margin-right: auto;" src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/8011d8c0-0999-4c1c-9825-44a124e9c504.png" alt="" width="700" height="467">
COBOL is a programming language that was designed exclusively for the business industry. It was the first-ever high-level programming language that was simple enough for people to read and write. COBOL is like the English language is structured the same way and can be understood by people who are not technical experts. The USA's Department of Defense introduced COBOL in 1959 to serve as a common language for business applications. Large companies generally use it in their financial and administrative systems because it is so easy to read and update.

What is COBOL?

COBOL has been around for over 60 years because of a few key reasons.
<img style="display: block; margin-left: auto; margin-right: auto;" src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/27af7093-fc0f-4ce0-918a-d63c9588d705.png" alt="" width="700" height="467">
&nbsp;
<h4>1. Dependable Systems</h4>
 Banks and government offices rely primarily on COBOL systems established many years ago. These systems have already become, thus, deeply incorporated into their everyday activities, so it is supposed to be null and void if the institutions attempt to rebuild them anew. Using COBOL technologies improves the running of the affiliates, and thus, it has become an irreplaceable component of these organizations. 
<h4>2. Cost Efficiency</h4>
 Fixing and updating COBOL systems usually costs less than developing new systems. The approach favors companies that do not have to build new software. Replacing COBOL systems would be prohibitively expensive so most companies continue to use and modernize them instead. 
<h4 dir="ltr">3. Proven Stability</h4>
 COBOL is very reliable in handling large transactions almost with zero errors. Thus it becomes an undisputed option in tasks of utmost importance where data precision is a must. COBOL is an aged and superior language that can handle big data tasks without a problem. 
<h4 dir="ltr">4. Skilled Workforce</h4>
 Even though it seems like there are fewer COBOL programmers, there are still many skilled people who know this language. Many companies train new workers to keep their IT operations running smoothly, making sure there are always capable COBOL programmers available.
&nbsp;

Why COBOL Survived

While COBOL is old, it has kept up with new technology to stay useful.
<img style="display: block; margin-left: auto; margin-right: auto;" src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/f477716f-e84f-4ea4-908d-a3827ebf11c6.png" alt="" width="700" height="467">
&nbsp;
<h4 dir="ltr">1. Integration Capabilities </h4>
COBOL can operate alongside modern systems and databases, which allows companies to include new features without rearranging everything. This also enhances businesses' COBOL applications with the latest technology. For instance, COBOL is compatible with modern APIs and cloud-based services, which simplifies organizations' work by introducing new technologies. 
<h4 dir="ltr">2. Cloud Computing </h4>
Some companies are making a migration of their COBOL applications to the cloud. This makes them more scalable and better for disaster recovery, but the reliability of COBOL still remains. Migration to the cloud allows companies to save bucks and work better with heavy operations. Cloud computing also has better security and compliance functionalities which are very important to many sectors. 
<h4 dir="ltr">3. Continuous Improvement </h4>
One of the ways COBOL has been able to keep abreast is by making some changes to the language one of which includes the use of new compilers. There are COBOL compilations that can be used in modern programming methods. This has allowed COBOL to be integrated into new development environments, enhancing COBOL&rsquo;s relevance. Glacier Point was COBOL&rsquo;s first attempt to modernize a language that was, in the past, rewritten anew from scratch to become current with the technology of the day. This occurs because companies will need to use applications that can interact dynamically with one another. But even this access nowadays can succeed off one resource only so cloud computing and SAAS have become very popular concepts.
&nbsp;

Modern Adaptations of COBOL

Even though COBOL is old, learning it can be useful, especially for those interested in working with legacy systems or industries that still rely on COBOL.
<img src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/69ad6849-fc3c-4628-97d3-b839ddd0e040.png" alt="" width="700" height="467">
<h4 dir="ltr">1. Online Resources </h4>
There are many online tutorials and courses for learning COBOL programming. These are good for beginners and experienced programmers. These resources allow for self-paced learning, making them accessible to everyone. Websites like Udemy, Coursera, and even YouTube offer courses on COBOL programming.
<h4 dir="ltr">2. Educational Programs </h4>
Some universities and technical schools offer courses in COBOL programming. These programs ensure that new generations of IT professionals can maintain and develop COBOL systems. Various professional training programs focus on COBOL, providing intensive learning opportunities for those aiming to specialize in this language. 
<h4 dir="ltr">3. Community Support</h4>
 There are communities of COBOL developers who offer support and share knowledge through forums, user groups, and networks. These communities help learners and professionals stay updated with best practices and new developments in COBOL programming. Websites like Stack Overflow and GitHub have active COBOL communities where developers can ask questions and share their knowledge.
&nbsp;

How to Learn COBOL

COBOL is a programming language that was invented in 1959. It is very old but still extremely important and widely used. COBOL is indeed one of the most common programming languages in the areas of business, finance, as well as government systems. To comprehend why COBOL is still relevant, we must explore its strengths and the problems it solves.

COBOL&rsquo;s ability to work with modern technology and its ongoing updates keep it important for many companies. Learning COBOL can be a good and rewarding skill for those looking for a stable, high-demand job. COBOL&rsquo;s history and continued use show its importance in the IT world. Understanding its role and updates helps explain why it&rsquo;s still a vital programming language. As long as there are important business systems needing stable and reliable performance, COBOL will continue to be essential.
&nbsp;

Wrapping Up

Why is COBOL Still Used? Get Expert Advice

Data science is dominantly related to statistics and deals with the extraction, analysis, and interpretation of big data. This field has become indispensable across the sector as it provides actionable insights and predictions. The role of data science becomes crucial with the explosion of data generated from digital activity, social media, and sensors, among many others.
&nbsp;
<img style="display: block; margin-left: auto; margin-right: auto;" src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/ef01d5b5-0539-4b00-ba4d-3e333046dc82.png" alt="" width="650" height="434">
&nbsp;
<h3 style="text-align: justify;">Impact on Emerging Technologies</h3>
Data science is at the heart of breakthroughs in machine learning and AI. Data scientists create models that process and analyze big data sets to predict patterns and behaviors driving progress in AI applications, from autonomous vehicles to individualized medicine.
&nbsp;
<h3 class="MsoNormal" style="text-align: justify;" align="center">The Rise of Big Data Analytics</h3>
Technologies in the management of large datasets. Big data technologies will make data processing tools and platforms available for industries that look forward to deriving competitive benefits from big data like Apache, Hadoop, and Spark.
&nbsp;
<img src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/f3c68916-2f85-4732-8fcf-a334fe526865.png" alt="" width="700" height="467">

The Ascendance of Data Science

Computer science is a scientific study dealing with the theoretical foundations and practical approaches of computation and the applications therein. It is a much bigger subject than data science, ranging from development to architectures of systems, including software development and algorithm optimization, with broad applications from computer graphics to complex scientific simulations.
&nbsp;
<h3 class="MsoNormal" style="text-align: justify;" align="center">Innovations in Software and Systems</h3>
As we push the boundaries of what our software is capable of, computer science will, in turn, be leading the innovations in cloud computing, cybersecurity, and network architecture. This is actually crucial, of course, not only for looking after the current digital needs but also serving as a very basis for the future technology infrastructure.
&nbsp;
<h3 class="MsoNormal" style="text-align: justify;" align="center">Advancements in Computational Theory</h3>
Theoretical advances in computer science translate to efficient algorithms and models that are imperative for processing information faster and more effectively. This will be of special importance as the world moves towards quantum computing, which is highly poised to change the way problems are solved.
&nbsp;
<img style="display: block; margin-left: auto; margin-right: auto;" src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/e2e81571-60c6-4723-9d5b-55a3f8ff06b8.png" alt="" width="700" height="467">

The Foundation of Computer Science

While it's tempting to view these fields through a competitive lens, their future likely lies in convergence rather than dominance. The integration of data science and computer science is making the way for more sophisticated technology solutions.
&nbsp;
<h3 class="MsoNormal" style="text-align: justify;" align="center">Enhanced Machine Learning Models</h3>
Combining computational techniques from computer science with the analytical methodologies of data science will further improve the machine learning models. Such a combination is thus very key for coming up with the required powerful, efficient, and scalable AI.
&nbsp;
<h3 class="MsoNormal" style="text-align: justify;" align="center">Smarter Data Processing Systems</h3>
With increasing complexity in the data, the systems processing the same must grow. Computer science comes with strong algorithms and architectures that, combined with the analytics focus of data science, are producing smarter and more responsive data-processing systems.
<img src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/8dd7f1a2-cedc-4f9d-b56f-a4bbc4816f95.png" alt="" width="700" height="467">

Convergence of Data Science and Computer Science

Looking ahead, the role of data science vs computer science is set to be central to technological advancements. If anything, their roles are changing to play an ever-greater role in collaboration to tackle the most daunting problems of our time, from climate change to global health. The interplay of these fields begins to yield great promise in fields as diverse as environmental monitoring to disease prediction. For example, when data scientists think of weather trends analysis, they have to use data methodologies, but from computer science, they will harness the computational power and the algorithms that will help them develop complex climatic conditions.
&nbsp;
Similarly, in health, there is the whole issue of how data science plays an important part in the recognition of disease trends and epidemiological patterns, while computer science supports the development of telehealth technologies and those for patient management. This synergy will be a huge enabler not only to take on the problems at hand but rather to get ready for incoming challenges, through the development of ever more resilient and adaptive systems. For example, AI and IoT integration, powered by data sciences and computer sciences, are set to redefine industries for process automation and improved decision-making.
&nbsp;
In similar regard, data-driven algorithms in smart cities would optimize everything from traffic management to energy consumption, thus leading to reduced carbon footprint and improved life quality. Therefore, essential responsibilities are played by computer science in altering these algorithms to better embed them into physical infrastructure. Convergence of these domains, with technological progress, can be expected to result in the types of innovation that were within the realm of science fiction, like self-driving cars and robotic healthcare.
&nbsp;
<img src="https://plaxonic.s3.ap-south-1.amazonaws.com/plaxonic/de11b08b-2f0f-4a49-8934-535f5775b5f5.png" alt="" width="700" height="467">

Future Trends and Predictions

The technology of tomorrow will rest on the confluence of data science with the power to predict and computer science with its power in operations. Starting from better urban planning to space exploration, most of the solutions tomorrow will be largely integrated with the above. If applied in urban planning, big data analytics turn the design of cities into that of efficiency and sustainability. Data science would predict how cities grow and what their future infrastructural needs would be, and computer science will develop the software solutions required to take care of those intricate systems, from traffic light synchronization to public safety networks.
&nbsp;
In other words, both these fields are moved together by the advancement of their collaboration through space exploration, which may lead to developing sophisticated navigation and predicting systems for spacecraft maintenance, to even autonomous robots for planetary exploration. Where data scientists are working to uncover insights on what is happening in extraterrestrial environments from vast quantities of space missions data, computer scientists build systems that are unyielding enough to host and operate in extreme circumstances.

Anticipating Technological Needs

As we stand on the brink of a technological renaissance, the debate of data science vs. computer science is fully conjectural right now and does not harbor any surprise&mdash;it's a debate over the blueprint of our digital future.&nbsp;Both data science and computer science play a central role in the modern technological ecosystem. As we get deeper into this digital age, the lines will blur more, and the synergies of such spaces only will be clearer, posing quite interesting questions for both future effects and development. Here, we follow the traces of both disciplines to understand which one of these might have the upper hand in the future. Which of these critical fields will steer the course of our technological evolution? Let&rsquo;s delve into this fascinating debate about data science vs computer science.

It is logical: either computer science will dominate as the ruling force, or data science will exert its domination as the upscaling conqueror. It's not one against the other; the two will be contributing and feeding into each other as they merge into one, shaping our technical future. With further development in these two areas, there would be more of a balance between how smoothly and ingeniously such disciplines integrate so as not to react to but predict technology requirements to foresee innovations that would be transformational and sustainable.

An Educated Workforce is an Empowered Workforce.

blogs

Why is COBOL Still Used? Get Expert Advice

Data Science vs. Computer Science: Which Will Shape Our Digital Future?

Do you need help with something? We are here to assist you.

Quick Links

Services

Resources

Careers