What is Data Science

 

Academic and business circles have paid a lot of attention to data science. New data science research institutes and organizations. Such as the New York University Center for Data Science and Columbia University Institute for Data Sciences and Engineering, have continued to appear on the scene. Data science courses and degree programs have been offered by a number of universities, including Fudan University, Columbia University, and the University of California at Berkeley.

Cleveland and Smith suggested that data science should be treated as its own field. Data scientists are employed by companies like Facebook, Google, EMC, IBM, and others. The data scientist is referred to as "the sexiest job of the 21st century" by Harvard Business Review. There are currently a number of perspectives on the definition of data science. However, no one definition is universal. We think that because data science is a new field of study. Its research goals are different from those of other, more established fields of science. Additionally, neither the social nor natural sciences have studied the scientific issues that data science addresses.

Our group has worked on data technology as well as Chinese-funded research projects that found that data in cyberspace have formed. What we refer to as datanature. The scientific study of data nature is known as data science.

Data science is currently viewed from a variety of perspectives.

In 2002, the Data Science Journal (codata.org/dsj/) was launched by the Committee on Data for Science and Technology (CODATA). CODATA defines data science as the technologies and methods for managing and utilizing scientific data for scientific research. Data science has been used to better describe the data-intensive nature of today's science and engineering as scientific data have become more accessible. Data technology is used by many disciplines to deal with scientific data from their areas. Bioinformatics, neuroinformatics, and social informatics are examples of X-informatics that emerged from this.

By analyzing gene expression data from over 2,500 ovarian tumor samples6, researchers at NuMedii, Inc., a big data company in Silicon Valley, predicted whether existing drugs could be used to treat ovarian cancer.

Ngrams on Google1 were used by mathematicians Aiden and Michel from Harvard University to study American history. They looked for the frequency of use of two phrases using Ngrams: "The United States is" and "The United States are" According to the search results, the two terms were used roughly equally prior to the American Civil War, but significantly more frequently after the war. This is thought to reflect public acceptance of the United States as a unified nation prior to and following the Civil War.

From this perspective, the term "data" mostly refers to the data that are created and used in scientific research. This emphasizes that data science is the management, processing, and utilization of scientific data to support scientific research, also known as the fourth paradigm of scientific research4 or data-intensive scientific research as it is currently referred to.The study of business data is the subject of data science.

Loukides talked about what data science is in 2010 and said that rather than working as a simple application with data5, data science should make it possible to create data products. Provost et al. (2013) pointed out that one of the fundamental ideas of data science is "extracting knowledge from data to solve business problems".

Numerous data scientists spend a significant amount of their time supporting research on BI methodology. A significant number of BI practitioners were transformed into data scientists in order to achieve this. Companies on the internet like Amazon, Google, LinkedIn, Facebook, and others hired data scientists and built data science teams. For the purpose of assisting management in making decisions, these data scientists investigate and evaluate business data. Amazon, for instance, generates high-quality product recommendations through collaborative filtering, and Facebook recommends connections to friends through the "People you may know" feature.

One aspect of data science, from this perspective, is the process of acquiring knowledge from business data for decision-making purposes. This is comparable to the work that BI scientists do. This is why many BI researchers are also known as data scientists. Data science, on the other hand, focuses more on common issues in the analysis of various business data, such as BI methodology issues, as opposed to BI issues.

The integration of artificial intelligence (AI), computing technology, and statistics is known as data science.

When discussing the role of data scientists, this point of view is frequently brought up. It is generally accepted that data scientists should have expertise in AI, statistics, computing technology, and other related fields. Additionally, it is held that data scientists are teams of statisticians, computer scientists, AI experts, and domain experts rather than individuals who specialize in a single area.

Statisticians, computer scientists, AI scientists, and other relevant experts make up the data scientist teams at Google and Facebook, for instance.

This view is straightforward: Data science is naturally a part of statistics, AI, and computing technology because they all use data to process and analyze.

By extracting knowledge from data, data science aims to solve problems in science and business.

Dhar talked about the business and research implications of data science in 2013. He said that "the study of the generalizable extraction of knowledge from data" is what data science is. He also said that a data scientist needs to know everything about statistics, machine learning, artificial intelligence, database management, and problem design in depth. The first three points of view can be seen as being combined into this one.

How does data science work?

The fundamental tenets that underpin the above definitions are that data science is used to support existing scientific research and management decision-making schemas and to acquire knowledge from data in certain relevant fields. However, despite the above work, data science is still far from being established as a novel scientific discipline. This is due to the fact that the subjects of their research are things found in nature, and the problems they try to solve are also problems that are dealt with in other fields of science.

Things from the natural world are increasingly being stored as data in cyberspace thanks to the development of digital equipment. Data have become increasingly diverse, complicated, and beyond human control as they are entered, generated, and created in cyberspace in a variety of ways. Humans are either unaware of or unable to comprehend a growing number of data. All data in cyberspace are referred to as datanature because they already exhibit characteristics of an independent world, similar to the natural world.

It is important to note that data in cyberspace can be divided into two categories. The first type of data is real data, which are things that come from the natural world. Personal information, which is data that is representative of a person's characteristics, is one example. The second type of data is known as virtual data, and it does not correspond to anything in the natural world. The term "virtual data" refers to instances of such data that do not have any natural context. Computer viruses, for instance, are neither natural viruses nor data representations of real viruses; Instead, they can only be found in cyberspace. The development of data nature has led to new research topics and objects of study.

These new subjects of study are data nature, or data, rather than things that are found in human society or the natural world. New scientific questions surround data nature. What is data nature's size? What is the global data growth rate? How does data travel through cyberspace? How should data nature's authenticity be established? The social sciences and the natural sciences do not address any of these issues. A new science must investigate these emerging scientific issues.

The study of data nature and data science together make up data science. The most fundamental aspect of it is knowledge extraction from data. The information gleaned from the data in data nature can be applied to natural and social science because some of the data do indeed represent actual events. 

Here, data science is defined as:

The theory, approach, and technology of studying data nature constitute data science. There are two main parts to it.

life sciences, healthcare, finance, transportation, and other fields have all benefited from our efforts since 1998. We have observed the similarity of data objects as one of a number of common data-related issues in scientific research and industrial applications over the years. We started looking into the idea of data science in 20099 after realizing that there is a significant need for research on the data itself. The annual International Symposium on Data Science and Dataology has been held here since 2010. We can discuss data science issues with researchers from computer science, the life sciences, astronomy, and other fields at the symposium.