Data science , also known as data-driven science , is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data in various forms, which is either structured or unstructured,   similar to data mining .
Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data.  It uses the techniques and theories of mathematics , statistics , information science , and computer science , in particular from the subdomains of machine learning , clustering , cluster analysis , data mining , databases , and visualization .
Turing award winner Jim Gray is a fourth paradigm of science ( empirical , theoretical , computational and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge .  
When Harvard Business Review called Expired it “The Sexiest Job of the 21st Century”  the term est devenu a buzzword , and is now Often applied to business analytics ,  or Even arbitrary use of data, or used as a sexed-up term for statistics.  There is no consensus on a definition or curriculum content.  Because of the current popularity of this term, there are many “advocacy efforts” surrounding it. 
The term “data science” (originally used interchangeably with ” datalogy “) has been used as a substitute for computer science by Peter Naur in 1960. In 1974, Naur published the Concise Survey of Computer Methods , which freely They are used in the field of data processing and are used in a wide range of applications.
In 1996, members of the International Federation of Classification Societies (IFCS) put in Kobe for their biennial conference. Here, for the first time, the term data science is included in the title of the conference (“Data Science, Classification, and Related Methods”),  after the term was introduced in a roundtable discussion by Chikio Hayashi. 
In November 1997, CF Jeff Wu gave the inaugural reading entitled “Statistics = Data Science?”  for his appointment to the HC Carver Professorship at the University of Michigan . In this lecture, he is a statistical data collector, data modeling and analysis, and decision making. In his conclusion, he initiated the modern, non-computer science, use of the term “data science” and advocated that statistics be renamed science and statisticians data scientists.  Later, he presented his reading entitled “Statistics = Data Science?” PC Mahalanobis Memorial Lectures.  These readings honorPrasanta Chandra Mahalanobis , an Indian scientist and statistician and founder of the Indian Statistical Institute .
In 2001, William S. Cleveland introduced data science as an independent discipline, extending the field of statistics to include “advances in computing with data” in his article “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics, “which was published in Volume 69, No. 1, of the April 2001 edition of the International Statistical Review / International Journal of Statistics.  In his report, Cleveland establishes six technical areas which he believes in the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory.
In April 2002, the International Council for Science (ICSU)  began the Data Science Journal ,  published their publication on the internet, applications and legal issues.  Shortly thereafter, in January, 2003, Columbia University Began publishing The Journal of Data Science , which provided a platform for all data workers and their views and exchange ideas. The journal was largely devoted to the application of statistical methods and quantitative research. In 2005, The National Science Board published “Long-lived Digital Data Collections: Defining Data Scientists in the 21st Century”, defining data scientists as “information and computer scientists, database and software programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection “whose primary activity is to conduct creative inquiry and analysis.” 
In the 2012 Harvard Business Review article “Data Scientist: The Sexiest Job of the 21st Century”,  DJ Patil claims to have coined this term in 2008 with Jeff Hammerbacher to their LinkedIn at LinkedIn and Facebook, respectively. He asserts that a scientist is “a new breed”, and that “a shortage of data scientists is becoming a serious constraint in some sectors”, but describes a much more business oriented role.
In 2013, the IEEE Task Force on Data Science and Advanced Analytics  was launched. In 2013, the first European Conference on Data Analysis (ECDA) was organized in Luxembourg, establishing the European Association for Data Science (EuADS) . The first international conference: IEEE International Conference on Data Science and Advanced Analytics was launched in 2014.  In 2014, General Assembly launched the student-paid bootcamp and the Data Incubator launched a competitive free data science fellowship.  . In 2014, the American Statistical Associationsection on Statistical Learning and Data Mining renamed its journal to “Statistical Analysis and Data Mining: The ASA Data Science Journal” and in 2016 changed its section to “Statistical Learning and Data Science.”  . In 2015, the International Journal on Data Science and Analytics  was launched by Springer to publish original work on data science and big data analytics. In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society “Data Science Society” at the third ECDA conference at the University of Essex , Colchester, UK.
Relationship to Statistics
The popularity of the term “data science” has exploded in business environments and academia, as indicated by a jump in job openings.  However, many critical academics and journalists see the distinction between science and statistics . Writing in Forbes , Gil Press argues that data science is a buzzword without a definition and has simply replaced ” business analytics ” in contexts such as graduate degree programs.  In the question-and-answer section of his keynote address at the Joint Statistical Meetings of the American Statistical Association , noted applied statistician Nate Silversaid, “I think data-scientist is a sexed up term for a statistician …. Statistics is a branch of science. Data scientist is redundant Slightly In Some Way and people shoulds not berate the term statistician. ”  Similarly, in business sector, multiple Researchers and analysts That state data scientists are far from alone in being white Sufficient Granting companies has real competitive advantage [ 25) and consider data scientists as well as big business data analysts , data scientists, big data developers and Big Data engineers . 
On the other hand, responses to criticism are as numerous. In a 2014 Wall Street Journal article, Irving Wladawsky-Berger compares the data science enthusiasm with the dawn of computer science . He argues data science, like any other interdisciplinary field, employs methodologies and practices from across academia and industry , but then it will morph into a new discipline . He brings attention to the sharp criticisms of computer science, now a well respected academic discipline, had to ounce face.  Likewise, NYU Stern’s Vasant Dhar, as do many other academic proponents of data science, argues more specifically in December 2013 which data is different from the existing practice of data analysis across all disciplines , which focuses only on explaining data sets . Data science seeks actionable and consistent pattern for predictive uses .  This practical engineering goal takes data science beyond traditional analytics . Now the data in these disciplines and applied fields that lacked solid theories , like health science and social science , could be sought and used to generate powerful predictive models. 
In an effort similar to David Dhar’s, Stanford Professor David Donoho , in September 2015, takes the proposal further by rejecting three simplistic and misleading definitions of data science in place of criticisms.  First, for Donoho, data science does not equate big data , the data is not a criterion for distinguishing data science and statistics.  Second, data science is not defined by the computing skills of large data sets, in which these skills are used for analyzes across all disciplines.  Third, data science is a more applied field where academic programsright now do not want to prepare data scientists for jobs, in which many graduate programs misleadingly advertise their analytics and statistics training as the essence of a science program.   As a statistician , Donoho , following many in his field, champions of the broadening of learning in the form of data science,  like John Chambers who urges statisticians to adopt an inclusive concept of learning from data,  or like William Cleveland who urges to prioritize extracting from data applicable predictive tools over explanatory theories.  Together, thesestatisticians envisioned an increasing inclusive applied field that grows out of traditional statistics and beyond.
For the future of data science, Donoho projects for an ever-growing environment for open science where data sets are used for academic publications .  US National Institute of Health has already announced plans to enhance reproducibility and transparency of research data.  Other big journals are likewise following suit. This way, the future of data science not only exceeds the boundary of statistical theories in scale and methodology, but data science will revolutionize current academia and research paradigms. As Donoho concludes, “the scope and impact of data science will continue to expand enormously in coming decades as scientific data and data about science itself become ubiquitously available.”
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