Cassandra Query Language

Big data is rapidly developing and has been used in various fields of science and has come up with new concepts and programs that need their own architecture to implement. One of the most important parts of this architecture is the database that can manage the required data. Unlike the regular databases, in this architecture, the database has no functional relationship, and since the data used in the big data is not necessarily structured, we need another type of database called NoSQL, and Apache Cassandra has one of the most appropriate fundamentals in NoSQL architecture.

The Cassandra database, a distributed…

Four levels of measurement

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The variable is a feature that is measured from people or objects in the statistical community and varies from member to member and usually represented by letters. After measuring the values of variables in the statistical population, if the statistical data from the population is qualitative, we use attribute or feature is instead of the variable. Quantitative values are obtained by counting or measuring with measuring instruments. This type of data is represented by numbers. Such as length, ‌ weight, pressure, temperature. Since these types of data are numerical, it is possible to perform mathematical calculations on them. Therefore, the…

Both are important in the data science ecosystem

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Qualitative data is also called stratified data because it can be categorized by category. For example, suppose a student reads a paragraph from a book in one of the class sessions. The teacher who listens to the reading gives feedback on how the child reads that paragraph. This is considered as an example of qualitative data if the teacher gives feedback based on fluency, tone of voice, pronunciation, clarity of pronunciation without giving the child a grade. It is very easy to understand the difference between quantitative and qualitative data. Qualitative data in defining attributes does not include numbers, while…

Which one is more popular?

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When you work with databases such as Excel or SQL Server. These databases usually try to make the data comprehensible to the computer. In fact, structured data that we have in Excel or relational databases such as SQL Server is data that can be performed on various operations with known computer algorithms. This is how we say the data is understood by the machine. But data such as daily news in text form, or data in movies and videos shared on social media, is different from the previous type. Due to their diversity, they are not able to be structured…

The most important foundation

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Data is actually a form of raw and intact information that is intended for various uses. This type of information may not be so tangled that it is incomprehensible until it is sorted. When a researcher starts collecting raw data for his or her research project, he or she does so using a tool called a questionnaire. Usually, questionnaires or survey forms have options such as A, B, C, D, etc. for themselves. Suppose this researcher wants to get the results of the questionnaire he has prepared. In the first place, by looking at a number of A, B, C…

The hidden treasure everywhere

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Data are facts that can be obtained through observation and research. Raw materials that have not yet been processed, such as the date and amount of an invoice, the number of employees in the company, and the details of the payroll. In a simpler example; Your date of birth is initially a piece of data against your age that becomes information for use after processing and announcing your age. We live in an information economy right now. Despite the fact that most organizations acknowledge this, they can not have a proper translation of it for their business and answer the…

Deep dive into natural selection method

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The first concept that programmers or application developers grapple with is the concept of algorithm. When a user or programmer is looking for a solution to a problem, he or she specifies a strategy or set of instructions needed to solve the problem, called an algorithm. One of the most cost-effective and simplest problem-solving techniques, in terms of computational load and time required to implement the algorithm, in the field of artificial intelligence is evolutionary computational methods. In general, evolutionary computational algorithms are based on the use of Darwin’s theory of evolution to implement computer programs. In the field of…


Reasonable algorithms for finding the fittest

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Optimization is an important and decisive activity in the management and system analysts would be able to design better systems when they can save time and costs with optimization methods. Many optimization problems in engineering and science are naturally more complex and difficult than can be solved by regular optimization methods such as mathematical programming.

An important category of optimization problems that have many applications is hybrid optimization is to search for the optimal point of functions with discrete variables. …

Computer Science, Optimization

Magnificent lessons learned from nature

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In finding the optimal answer with heuristic algorithms, we usually use searching methods by trial and error process. There is no guarantee that a solution will be found in this method, as many other popular methods may be more effective and efficient. In general, heuristic algorithms are considered local search-based methods because their searches focus on local variables; However, heuristic algorithms can still be considered as the best ways to solve optimization problems, especially when time constraints are also important in problem-solving. But as I said before, (Please read the previous story about heuristics, heuristic solutions: the shortcut for optimization…

Computer Science, Optimization

The shortcut for optimization problems

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The heuristic is a computational process that finds a solution for a problem with trial and error. There are criteria, methods, or principles for deciding between several policy options and selecting the most effective one to achieve the desired goals. These achievements are the result of trade-offs between two kinds of requirements: the need to build simple criteria and at the same time the ability to distinguish right from wrong and can be a thumb calculation used to guide a set of actions.

Let’s assume a chess grandmaster who is faced with a choice between several possible moves. He(or she)…

Saman Siadati

Data Science and Machine Learning Researcher

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