Best Laptop For Computer Science
Top-10 Best Laptop For Computer Science reviewed - 2022 update
Computer science students are in demand on the labor market, and this profession is becoming one of the most profitable.
Especially in the context of the 2022 pandemic, when it is desirable to work from home.
A computer or laptop in this case becomes a working tool. But not every machine is suitable for the workflow - the model must meet certain requirements. When compiling the rating, we took into account only those laptops for computer science students that are superior to competitors in many ways: laptop for computer science students.
We also took into account the opinion of experts, consumer reviews. The selection of models was carried out on the basis of disadvantages and advantages, price, cost-effectiveness ratio of the configurations of each laptop.
Lenovo ThinkPad X1 Extreme Gen2
An impressive laptop for computer science students that can connect multiple monitors at once, handle the toughest tasks and run heavy programs without compromising performance. This model is also equipped with a 4K touch panel display. Characteristics:
- Large amount of memory - operational up to 64 GB, built-in - up to 4000 GB. This volume will be enough to install many programs and work with heavy loads.
- Quality build.
- The battery can provide up to 15 hours of operation. In addition, fast charging technology is provided.
- 15.6-inch display with a resolution of 3840 x 2160, which creates a high-quality and bright image.
- The fingerprint reader and facial recognition are responsible for security.
- The top cover is made of carbon fiber.
HUAWEI MateBook X Pro
Another model that professional programmers cannot pass by. The laptop of the Chinese company impresses with its functionality and performance. Character traits:
- 10th generation Intel 4-core processor;
- the amount of RAM is up to 16 GB, built-in - up to 1000 GB;
- 13.9-inch touch panel display, 3000 x 2000 resolution, 3:2 aspect ratio;
- Powerful battery that supports battery charge up to 13 hours.
It only takes one touch to turn your mobile device and laptop into one powerful machine. You don't even need to have internet access to do this. The smartphone screen is displayed on the monitor, which allows you to quickly and easily open and modify files, manage other tasks.
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The company claims that their 17-inch laptop is the lightest among similar models - 1.3 kg. In addition, manufacturers have provided all the necessary functions and features that are necessary for experienced programmers. In addition to the functionality, users will be pleased with an interesting and stylish design. Main characteristics:
a 17-inch screen with a resolution of 2560 by 1600 pixels, an aspect ratio of 16 to 10 - with such indicators, the model has a fairly small weight, which makes it convenient to carry it with you;
RAM - up to 16 GB, built-in - up to 512 GB;
The battery allows you to maintain a charge up to 19 hours of use.
Such a machine will be a real find for beginners and experienced professionals.
What is included in Computer Science
Computer Science is very broad, so we will not be able to give a complete list of the areas that fall within it. Here are some examples of the theoretical and practical disciplines that belong to it.
Mathematical analysis, linear algebra and other disciplines are also important but it is discrete mathematics that has the greatest connection to computer science. It studies "discontinuous", finite, i.e. discrete structures. A huge number of algorithms are based on this mathematics and are used in various branches of IT. Discrete mathematics includes graph theory, finite automata, combinatorics and many other areas.
Theoretical computer science
This is a fundamental science that deals with information: how it is represented, stored and transmitted. Theoretical computer science deals with abstract concepts and theories. The notion of "fundamental" means that this science does not imply creating anything in practice: it can describe a new approach to storing information, but not implement a machine that stores it that way. Theoretical computer science includes information theory and coding theory - the latter is about converting information into codes. It also includes the study of algorithms and the structure of programming languages.
Cryptography and information security
This field studies methods of protecting information from unauthorized access, interception or eavesdropping. Everyone who deals with the web needs a basic understanding of information security, pentestlers, ethical hackers and IS specialists need a thorough knowledge of it.
Computer science is not the same as programming, although the fields are linked. CS studies not so much the features of languages and how to use them, but their inner workings as a whole. It is how programming languages are structured, what their structure is, how they are implemented and what they are built on. Designing programming languages, their classification, analysis relate to computer science.
This section explains how computer hardware is organised internally and how its component parts work: the processor, various nodes and memory blocks, and other structures. The discipline focuses on the structure of the inner workings. To describe how things work in terms of physics and electrical engineering, there are other fields such as computer engineering. It also falls under Computer Science.
Primarily, knowledge of computer architecture is needed by low-level and system programmers. Most popular languages now work at a "high" level, i.e. close to human. But such languages are written at a lower level, and those, in their turn, at an even lower level. The level is progressively lowered down to machine codes. So eventually any developer may need to understand the architecture.
Artificial intelligence theory
This huge field includes everything that has to do with the intelligent behaviour of computer systems. It includes robotics, computer vision and computer processing of natural language - also called NLP. This also includes machine learning, neural network theory and more.
Such knowledge is mostly needed by professionals in relevant industries. For example, a developer in Machine Learning will need it from his first days of work, while a frontrunner may not encounter it for years. But having at least a superficial understanding of AI is still worthwhile.
This unit covers the network: how it is set up, how it communicates and how it works. It describes network protocols, their idiosyncrasies, and security. An understanding of networks is essential for anyone dealing with the development of websites, applications, or distributed systems. They are important to information security specialists, administrators, engineers and many others.
A separate discipline exists around databases, which are structured repositories of information. It describes approaches to data storage, linking and access. All developers need some knowledge of this discipline. Database administrators and people who specialize in RDBMS - database management systems - must have a deep understanding of it.
The study, creation and application of algorithms is a large discipline in its own right. It has a huge practical application: algorithms are needed to solve complex application problems or to optimise code. A simple example is sorting: different algorithms have different efficiency and speed, and one can choose one's own algorithm to solve a particular problem. And there are a lot of such examples. Therefore, a good IT-specialist must be familiar with the basic algorithms from his field.