{"id":6912,"date":"2012-04-27T02:41:07","date_gmt":"2012-04-27T02:41:07","guid":{"rendered":"http:\/\/www.smartdatacollective.com\/index.php\/post\/evolution-what-data-science\/"},"modified":"2012-04-27T02:41:07","modified_gmt":"2012-04-27T02:41:07","slug":"evolution-what-data-science","status":"publish","type":"post","link":"https:\/\/www.smartdatacollective.com\/evolution-what-data-science\/","title":{"rendered":"The Evolution of “What is Data Science?”"},"content":{"rendered":"

I\u2019m in the process of researching the origin and evolution of data science as a discipline and a profession. Here are the milestones that I have picked up so far, tracking the evolution of the term \u201cdata science,\u201d attempts to define it, and some related developments.  I would greatly appreciate any pointers to additional key milestones (events, publications, etc.)<\/em>.<\/p>\n

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I\u2019m in the process of researching the origin and evolution of data science as a discipline and a profession. Here are the milestones that I have picked up so far, tracking the evolution of the term \u201cdata science,\u201d attempts to define it, and some related developments.  I would greatly appreciate any pointers to additional key milestones (events, publications, etc.)<\/em>.<\/p>\n

1974<\/strong> Peter Naur<\/a> publishes Concise Survey of Computer Methods<\/em> in Sweden and the United States. The book is a survey of contemporary data processing methods that are used in a wide range of applications. It is organized around the concept of data as defined in the IFIP<\/em><\/a> Guide to Concepts and Terms in Data Processing<\/em>, which defines data as \u201ca representation of facts or ideas in a formalized manner capable of being communicated or manipulated by some process.\u201c The Preface to the book tells the reader that a course plan was presented at the IFIP Congress in 1968, titled \u201cDatalogy, the science of data and of data processes and its place in education,\u201c and that in the text of the book, \u201dthe term \u2018data science\u2019 has been used freely.\u201d Naur offers the following definition of data science: \u201cThe science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.\u201d<\/p>\n

1977<\/strong> The International Association for Statistical Computing<\/a> (IASC) was founded as a Section of the ISI<\/a>. \u201cIt is the mission of the IASC to link traditional statistical methodology, modern computer technology, and the knowledge of domain experts in order to convert data into information and knowledge.\u201d<\/p>\n

1989<\/strong> Gregory Piatetsky-Shapiro<\/a> organizes and chairs the first Knowledge Discovery in Databases (KDD) workshop<\/a>. In 1995, it became the annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).<\/p>\n

1996<\/strong> Members of the International Federation of Classification Societies<\/em> (IFCS)<\/em><\/a> meet in Tokyo for their biennial conference. For the first time, the term \u201cdata science\u201d is included in the title of the conference (\u201cData science, classification, and related methods<\/a>\u201d). The IFCS was founded in 1985 by six country- and language-specific classification societies, one of which, The Classification Society<\/em><\/a>, was founded in 1964. The aim of these classification societies has been to support the study of \u201cthe principle and practice of classification in a wide range of disciplines\u201d(CS), \u201cresearch in problems of classification, data analysis, and systems for ordering knowledge\u201d(IFCS), and the \u201cstudy of classification and clustering (including systematic methods of creating classifications from data) and related statistical and data analytic methods\u201c (CSNA bylaws). The classification societies have variously used the terms data analysis, data mining, and data science in their publications.<\/p>\n

1997<\/strong> Launch of the journal Knowledge Discovery and Data Mining<\/a>: \u201cAdvances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing. KDD is concerned with issues of scalability, the multi-step knowledge discovery process for extracting useful patterns and models from raw data stores (including data cleaning and noise modelling), and issues of making discovered patterns understandable.\u201d<\/p>\n

2001<\/strong> William S. Cleveland<\/a>  (then at Bell Labs, now at the Department of Statistics at Purdue University) publishes “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics<\/a>.” It is a plan \u201cto enlarge the major areas of technical work of the field of statistics. Because the plan is ambitious and implies substantial change, the altered field will be called \u2018data science.\u2019” The plan \u201csets out six technical areas for a university department\u201d: Multidisciplinary Investigations, Models and Methods for Data, Computing with Data, Pedagogy, Tool Evaluation, and Theory. Cleveland puts the proposed new discipline in the context of computer science and the contemporary work on data mining: \u201c\u2026the benefit to the data analyst has been limited, because the knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited. A merger of knowledge bases would produce a powerful force for innovation. This suggests that statisticians should look to computing for knowledge today just as data science looked to mathematics in the past. \u2026 departments of data science should contain faculty members who devote their careers to advances in computing with data and who form partnership with computer scientists.\u201d<\/p>\n

April 2002<\/strong> The Data Science Journal<\/a><\/em> is launched, publishing papers on \u201cthe management of data and databases in Science and Technology. The scope of the Journal includes descriptions of data systems, their publication on the internet, applications and legal issues.\u201d The journal is published by the Committee on Data for Science and Technology (CODATA<\/a>) of the International Council for Science (ICSU).<\/p>\n

January 2003<\/strong> The Journal of Data Science<\/em><\/a> <\/em>is launched: \u201cBy \u2018Data Science\u2019 we mean almost everything that has something to do with data: Collecting, analyzing, modeling…… yet the most important part is its applications — all sorts of applications. This journal is devoted to applications of statistical methods at large\u2026. The Journal of Data Science will provide a platform for all data workers to present their views and exchange ideas.\u201d<\/p>\n

September 2005<\/strong> The National Science Board<\/a> publishes \u201cLong-lived Digital Data Collections: Enabling Research and Education in the 21st<\/sup> Century<\/a>.\u201d One of the recommendations of the report reads: \u201cThe NSF, working in partnership with collection managers and the community at large, should act to develop and mature the career path for data scientists and to ensure that the research enterprise includes a sufficient number of high-quality data scientists.\u201d The report defines data scientists as \u201cthe information and computer scientists, database and software engineers and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection.\u201d<\/p>\n

July 2008<\/strong> The JISC<\/a> publishes the final report of a study it commissioned to \u201cexamine and make recommendations on the role and career development of data scientists and the associated supply of specialist data curation skills to the research community. \u201c The study\u2019s final report, \u201cThe Skills, Role & Career Structure of Data Scientists & Curators:  Assessment of Current Practice & Future Needs<\/a>,\u201d defines data scientists as \u201cpeople who work where the research is carried out \u2013 or, in the case of data centre personnel, in close collaboration with the creators of the data \u2013 and may be involved in creative enquiry and analysis, enabling others to work with digital data, and developments in data base technology.\u201d<\/p>\n

January 2009<\/strong> Harnessing the Power of Digital Data for Science and Society<\/em><\/a> is published. This report of the Interagency Working Group on Digital Data to the Committee on Science of the National Science and Technology Council states that \u201cThe nation needs to identify and promote the emergence of new disciplines and specialists expert in addressing the complex and dynamic challenges of digital preservation, sustained access, reuse and repurposing of data. Many disciplines are seeing the emergence of a new type of data science and management expert, accomplished in the computer, information, and data sciences arenas and in another domain science. These individuals are key to the current and future success of the scientific enterprise. However, these individuals often receive little recognition for their contributions and have limited career paths. Critical challenges in achieving our strategic vision include providing an effective pipeline of data professionals to ensure that the needs and opportunities of the future can be met and providing these professionals with appropriate rewards and recognition.\u201d The report discusses the emergence of \u201cnew information disciplines\u201d and lists a few examples:<\/p>\n