What is Computer Science (CS) and for whom is it suitable?
If we want to explain in the simplest way, we have to say that computer science is the study of information (data) and the method of using them (algorithms) to solve problems theoretically and practically.
Computer science does not mean studying computers and does not always require the use of computers. It is also possible to compute data and algorithms using paper and automation, and this science is more dependent on mathematics than on computers, which is why some people prefer to use the word informatics instead of computer science.
Computer science often interferes with three other areas, but despite their close connection, they should not be confused:
• Computer engineering: involves the study of data and algorithms in the field of hardware and seeks to answer questions such as How to communicate between electronic components, how to design microprocessors and ways to improve the performance of chip-sets.
• Software Engineering: This branch can be considered as an applied computer science. Computer scientists are looking for ideas in computer science, but software engineers are writing applications that integrate these theories with algorithms.
• Information Technology:Includes the use of previously designed software and hardware. IT professionals help maintain networks and provide solutions when problems occur in network applications and systems.
Despite these differences, most people use the term computer science to refer to all areas of programming, analysis, and use of data and work with software and hardware, while computer science uses fields and disciplines. It takes a lot.
If you are planning to study computer science, you should know that the same courses and courses are not taught in different universities. These universities do not even agree on the branches of computer science, let alone offer the same courses.
The following is a list of different computer science courses taught at different universities. As you can see, computer science covers a wide range of disciplines:
• Artificial Intelligence: The development of machines that have cognitive abilities such as thinking, speaking, reasoning, and problem solving are involved in topics such as linguistics, psychology, and psychology. Neurology is combined with computer science. Machine learning is also a subset of this branch that examines the ability of machines to learn, evolve, and recognize patterns in data.
• Bioinformatics:Knowledge of computer science is used to measure, analyze, model, and understand complexities in biology, and includes large-scale data analysis, heavy computing, data simulation, molecular modeling, and more.
• Computational theory: The knowledge of studying algorithms and mathematical evidence. This branch is not only concerned with creating new algorithms and improving existing algorithms, but also with improving methods and proving theories.
• Computer graphics: involves studying the methods of using data and converting them into shapes that are visible and understandable to people. This category includes topics such as realistic images, dynamic images, 3D modeling, animation and visual images.
• Game development:Production of computer, mobile and web-based games fall into this category. Game engines vary in price and usage, and often include unique algorithms and structures that are optimized for real-time interaction.
• Networking: The knowledge of studying computer systems is distributed and how to improve calculations between networks.
• Robotics: Knowledge of the production and development of algorithms used in robotic machines and includes improving the science of robot movement, human-robot interaction, environmental interactions, interactions between robots, virtual intermediaries, and more.
• Safety:Development of algorithms, methods and software to protect computer systems against threats, malware and abuses fall into this category. This category includes network security and cloud computing, personal computer security, phone security, email security, antivirus software, and encryption and decryption.
To graduate with a degree in computer science from a college or university, you must learn the following concepts:
• How to perform computer systems at hardware and software levels
• How to write code in different programming languages
• The nature and application of data structures And algorithms
• Computational concepts (for example, formal logic, graph theory, etc.)
• How to design a compiler, operating system and computer
Do you have the necessary features to study computer science?
I never ask people to study computer science just because they don’t have the qualities they need. If you want to study in this field, then be sure to do so. Excessive effort and perseverance in all tasks will overcome the existing shortcomings.
However, having certain characteristics can make you enjoy studying in this field and have a better chance of success. Receiving a bachelor’s degree costs four years of your life, so have enough information to avoid wasting your best years in the field in which you are going to study. Appropriate people to study computer science have the following characteristics:
• They are inherently curious and questioning.
• They find themselves forced to solve problems and puzzles.
• Their way of thinking is logical and step-by-step.
• Look at things from unusual angles.
• Looking to learn new things every day.
• Endure long periods of failure.
• Know how to search the web to find answers to their questions.
Problem solving in computer science, software engineering and information technology is a key skill. If you are not naturally curious and naturally do not like to worry about various issues, you will not have much fun after studying computer science.
In addition, computer science is a discipline with constant change and rapid growth, so if you are not interested in continuous learning of languages, library functions, programs, software and new code editors, you will quickly get tired of studying in this field unless Just content with the old.
Studying computer science requires a lot of creativity. In this field, unlike a writer, painter or musician, you have to code from scribes who need to think outside the box and practice regularly. Programming Obstacles One of the problems for programmers is that you have to go through them over and over again to make the right decisions.
If you do not have any of the above features, you may not be a good option to study in this field, but if you feel that you have the mentioned features, then we must congratulate you because it is a difficult but valuable field.You enter.
To be successful in this field, you need to know one more thing: at the beginning of entering this field, you can examine and try different fields as much as you can. You may be surprised at your talent in a particular field. But once you’ve found the right background, focus on it and try to become an expert. It may be interesting to jump from one branch to another as a hobby, but such people rarely achieve high expertise in a job.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
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