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It deals with the collection of techniques, tools, and theories required to solve computer mathematical models of problems.
This software system is used to retrieve, store and run queries on data, and it is basically a computerized data-keeping system.
It is the engineering of computer systems through the careful design of its organizations while using an integrating software technique and innovative mechanisms to achieve a set of performance goals.
It is the process used to extract usable data from a large set of raw data, and it is used to segment the data and evaluate the probability of future events.
It is a branch of computer science and artificial intelligence that deals with the use of algorithms and data to imitate intelligent human behaviour.
It is an operating system that connects several computers on the network and manages the network resources.
It is the process of data representation by interacting with them to obtain insights into the data. It is the technique to create diagrams, animations, images, etc., to communicate a message.
It is a series or process used to develop software. It is designed to describe the life cycle of software and its codified information.
These are computer languages used by developers or programmers to communicate with programmers. It is basically a set of instructions and grammatical rules provided to the computing device to perform specific tasks.
It is a software development methodology aimed at defining methods, models, and tools that are suitable for the efficient and precise representation of software-intensive systems.
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The growth of technology is creating a heavy demand for computer science. Moreover, studying this discipline will equip you with the relevant skills and knowledge required to indulge yourself in the ever-developing and exciting industry. If you wonder what the importance of learning this complicated discipline is, take a look.
The requirements to get enrolled in a computer science course are as follows:
It is a research process used to address next-generation issues within computer architecture. This addresses the grand challenge of parallel computation and brings in techniques from another field. It also creates innovative ideas for architecture based on power budget issues, novel substrates, micro-architectural circuits, etc.
Some of the examples of machine learning are:
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