Big data:
Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief intro...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2021
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| Schriftenreihe: | Cambridge elements
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| Schlagworte: | |
| Online-Zugang: | BSB01 UBG01 UBW01 Volltext |
| Zusammenfassung: | Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Feb 2021) |
| Beschreibung: | 1 Online-Ressource (77 Seiten) |
| ISBN: | 9781108588676 |
| DOI: | 10.1017/9781108588676 |
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Datensatz im Suchindex
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| dewey-full | 005.7 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
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| discipline | Informatik |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:50:37Z |
| indexdate | 2024-07-10T09:05:26Z |
| institution | BVB |
| isbn | 9781108588676 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032604675 |
| oclc_num | 1242729349 |
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| owner_facet | DE-12 DE-473 DE-BY-UBG DE-20 |
| physical | 1 Online-Ressource (77 Seiten) |
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| publishDate | 2021 |
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| publisher | Cambridge University Press |
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| spelling | Pietsch, Wolfgang (DE-588)1144305268 aut Big data Wolfgang Pietsch Cambridge Cambridge University Press 2021 1 Online-Ressource (77 Seiten) txt rdacontent c rdamedia cr rdacarrier Cambridge elements Title from publisher's bibliographic system (viewed on 05 Feb 2021) Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed Big data Induction (Logic) Datenanalyse (DE-588)4123037-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Wissenschaftsphilosophie (DE-588)4202787-1 gnd rswk-swf Big Data (DE-588)4802620-7 s Datenanalyse (DE-588)4123037-1 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Wissenschaftsphilosophie (DE-588)4202787-1 s Erscheint auch als Druck-Ausgabe 978-1-108-70669-8 https://doi.org/10.1017/9781108588676 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Pietsch, Wolfgang Big data Big data Induction (Logic) Datenanalyse (DE-588)4123037-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Big Data (DE-588)4802620-7 gnd Wissenschaftsphilosophie (DE-588)4202787-1 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4193754-5 (DE-588)4802620-7 (DE-588)4202787-1 |
| title | Big data |
| title_auth | Big data |
| title_exact_search | Big data |
| title_exact_search_txtP | Big data |
| title_full | Big data Wolfgang Pietsch |
| title_fullStr | Big data Wolfgang Pietsch |
| title_full_unstemmed | Big data Wolfgang Pietsch |
| title_short | Big data |
| title_sort | big data |
| topic | Big data Induction (Logic) Datenanalyse (DE-588)4123037-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Big Data (DE-588)4802620-7 gnd Wissenschaftsphilosophie (DE-588)4202787-1 gnd |
| topic_facet | Big data Induction (Logic) Datenanalyse Maschinelles Lernen Big Data Wissenschaftsphilosophie |
| url | https://doi.org/10.1017/9781108588676 |
| work_keys_str_mv | AT pietschwolfgang bigdata |