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Название: Data Science In The Library: Tools and Strategies for Supporting Data-Driven Research and Instruction
Автор: Joel Herndon
Издательство: Facet Publishing
Год: 2022
Страниц: 224
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.2 MB
The last two decades represent an unprecedented period in the history of data analysis. As the cost of technology has steadily decreased, access to sophisticated data tools has increased, expanding the audience for data-informed research and decision making. At the same time, new areas of research and research methodologies are now possible with the rapid growth of online data produced as a byproduct of digital commerce, file sharing and social media. Together, this confluence of inexpensive computing, plentiful data and accessible tools has created a new interdisciplinary area of research that harnesses the traditional disciplinary expertise of statisticians and computer scientists to explore a wide range of data-related questions. As more researchers and companies embrace data-driven approaches, the phrase ‘Data Science’ has become an increasingly popular term to describe this growing area of research. In its current form, the term ‘Data Science’ is easier to define by its application than by theory. It is widely understood to include a diverse range of computational, data-driven approaches to research and business analytics. In academia, this interdisciplinary space comprises a broad range of methodologies, including Machine Learning, social media analysis, spatial analytics, text analysis and web analytics to name a few.
Автор: Joel Herndon
Издательство: Facet Publishing
Год: 2022
Страниц: 224
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.2 MB
The last two decades represent an unprecedented period in the history of data analysis. As the cost of technology has steadily decreased, access to sophisticated data tools has increased, expanding the audience for data-informed research and decision making. At the same time, new areas of research and research methodologies are now possible with the rapid growth of online data produced as a byproduct of digital commerce, file sharing and social media. Together, this confluence of inexpensive computing, plentiful data and accessible tools has created a new interdisciplinary area of research that harnesses the traditional disciplinary expertise of statisticians and computer scientists to explore a wide range of data-related questions. As more researchers and companies embrace data-driven approaches, the phrase ‘Data Science’ has become an increasingly popular term to describe this growing area of research. In its current form, the term ‘Data Science’ is easier to define by its application than by theory. It is widely understood to include a diverse range of computational, data-driven approaches to research and business analytics. In academia, this interdisciplinary space comprises a broad range of methodologies, including Machine Learning, social media analysis, spatial analytics, text analysis and web analytics to name a few.