Data Science
Data science; Scientific methods, analysis methods, algorithms, and database management systems to obtain information and results, and the examination and processing of data.
As the devices and technology, we use to develop, the data flow increases more. That’s why data science becomes so important for companies using big data. Data science manages the decision-making processes of companies and is used to produce meaningful results from data.
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Data scientists are information and solution-oriented people who analyze the data with software programs, draw meaningful results from the data and visualize the data by using all data.
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Data Science History
Data science first emerged in the 1960s and is advancing rapidly until today. In the 1960s, room-sized systems were built and statistical problems were started to be run on these systems. Results were obtained after long periods of weeks and months.
In the 1970s, data and data science definitions started to be used and spread for the first time. In the 1990s, companies achieved great success by collecting people’s data and using this information in marketing. At the same time, data mining efforts are increasing and new algorithms are developed towards the end of the 1990s. With the development of the internet and technology, research is getting faster. Together with data centers, even huge statistical problems can be solved in a very short time, within hours and minutes.
In the 2000s, statistical and algorithmic modeling studies are carried out by combining data science and computer science. New algorithms are being developed. These algorithms are mostly used by companies. At the same time, companies used data science as the primary factor for competition in the 2000s. In the 2010s, people who are interested in data start working as data scientists. It has become one of the most needed jobs today.
R:
R is a programming language. It is mostly used in areas such as statistical calculation and graphics. So it is like the cornerstone of data science. The R programming language was released in 1993. It is open-source and free. It is one of the most used programs in data science. You can use it in many areas from basic statistical calculations to data visualization.
Python:
Python was first released in 1994. Python is a high-level programming language. Python is built for almost every purpose. Whatever you do, you can do it with python.
Python is an interpreted, interactive, object-oriented programming language. It includes modules, exceptions, dynamic typing, very high-level dynamic data types, and classes. — Python Software Foundation
It is one of the most used high-level languages in data science.
SQL:
SQL (structured query language) is a programming language used for operations such as creating and managing data. It is a sub-language used in databases. SQL originated in 1974. There are paid and free options.
These three programming languages are the most used in data science.
Data Science Career Areas
Data Analyst:
Data analyst manages processes such as extracting data from databases, collecting, analyzing and drawing conclusions. They mainly analyze statistical data.
Machine Learning Specialist:
Machine learning experts are needed where there are heavy data flow and big data. Must be very good at math, statistics, data analysis, and software.
Data Engineer:
The difference is that data engineers are similar to machine learning specialists, but machine learning experts are a little more focused on mathematics and statistics. It is mostly preferred by companies that establish their infrastructure.
Data Scientist:
Data scientists are also experts in statistics and programming. They know topics such as data analysis, data management, and analysis.
Today I told you about data science. In the next article, I will explain the basic statistics. You can also read my other articles about the software.
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