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Data science is an interdisciplinary ground that uses technical devices, processes, algorithms, and systems to extract knowledge and information from structured and unstructured data and apply knowledge and insights actionable insights from various application areas. And also,  Data science refers to data mining, machine learning, and big data.

Data science is a concept of standardizing statistics, analysis, computing, and related methods. To use data to understand and analyze natural phenomena.  It employs techniques and theories from mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computing and information science. Turing Prize winner Jim Gray envisioned data science as the ‘fourth paradigm’ of science empirical, theoretical, computational, and now data-driven, claiming that ‘everything in science changes because of the effects information technology and data flow

How data science was born

The origins of this correction date back to 1962, when John Tukey discussed the future of aesthetic mathematics on ethics as an empty science, from 1974 when Peter Near  focused on data and coined the term “data science.” In 1977, the time was incorporated into various associations and congresses in statistics and information technology.

More recently, Turing Prize winner Jim Gray conceived of data science as the “fourth paradigm” of science empirical, theoretical, computational, and now data-driven and found that “everything in science is changing due to the impact of technology.” and the flow of information and data. Therefore, For more information on the origins of data science, see the famous Forbes article “A Very Brief History of Data Science. And also, Data science is developing as an interdisciplinary field that includes: programming;  mathematics and statistics; And also,  experience in teaching, as shown in Drew Conway’s Venn diagram (2010).

What are the applications of data science

Data science is one of the professions that currently have the greatest need for skilled professionals worldwide. Applies to almost all areas: However,  Product recommendation systems typically use data science to extract information from search engines and social media such as stories

In the field of TECHNOLOGICAL or logical innovations such as autonomous vehicles, Scientific Data uses real-time data (from various types of sensors) to create a visual map of your surroundings and make essential decisions about driving and cornering, braking, accelerating, etc.

As scientists worked in both the world and agricultural weather forecasting, data scientists collect information from satellites, radars, ships, and aircraft to create models that can predict the weather and predict impending natural disasters with great accuracy. And also, It will help you take the right action at the right time and avoid as much damage as possible. Therefore,  In this regard, an interesting discussion by Kirk Bourne, Ph.D., chief data scientist at Booz Allen Hamilton, about how scientific GRAPHICS data can help in the new era of significant and complex data.

The start of a career in data science is promising.

They have access to many future job opportunities and an excellent opportunity to work in technology with room for experimentation and creativity. And also, If you want to learn about the most critical strategies for moving down this path, check out our article on How to Become a Data Scientist. You can also experience  first-hand, working as a data analyst in different companies to turn data into sound knowledge.

Data specialist

Data scientists or data scientists are professionals, mostly with multidisciplinary experience, trained and with the necessary curiosity to discover the complex world of big data. And also, You can generate and analyze the vast amount of unstructured data that we create every day. And also, You are responsible for identifying and combining possible sources of information and refining the result set. Therefore, Data scientists help managers make decisions about switching from ad hoc data analysis to ongoing communication with them.

Data scientists are responsible for finding patterns in data. And also, making insights from them, and communicating the effects of the insights from their analyzes to identify new business opportunities. Therefore, They advise executives and product managers on the value of data for products, processes, and solutions.

Data mathematical

Although data scientists can at first glance be imagined as people with a robust analytical profile and a lot of statistical and mathematical knowledge, this impression would be completely wrong. Rather, they are characterized by their scientific content; One of the dominant aspects of your personality is an intense interest, a desire to go beyond the problem, find the questions it contains, and refine them until you come up with a clear set of hypotheses that can be tested on complex data. Because of this, some of the most renowned data scientists from the world’s leading technology companies work in non-traditional areas such as physics and social sciences.

Data scientists are motivated not to create beautiful reports with structured information. And also, there are already financial analysts for that. And also, What motivates data scientists are to develop new things, not just advice. Therefore,  You want to create solutions that work and have an innovative effect on companies and consumers.

And also, You can think of data scientists as a mix of hackers, analysts, communicators, and consultants. Therefore, Individuals with the technical knowledge to process and analyze large amounts of data and have a sufficient understanding and understanding of the business and the ability to transfer data efficiently. A scarce combination, but highly effective!

It would help if you were also well-versed in probability and statistics. And also,  It is where the R and Python programming languages ​​can come in handy.

Finally, you should be aware of major big data frameworks like Hadoop. Therefore, Knowledge of cloud computing infrastructure. And also, in mainstream databases from SQL and NoSQL.

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Nate silver

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Andrew gelman

Columbia university

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