The 9 pitfalls of data science:
Using bad data -- Putting data before theory -- Worshiping math -- Worshiping computers -- Torturing data -- Fooling yourself -- Confusing correlation with causation -- Being surprised by regression toward the mean -- Doing harm -- Case study : the Great Recession.
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Oxford
Oxford University Press
2019
|
| Edition: | First edition |
| Series: | Oxford scholarship online
|
| Subjects: | |
| Online Access: | UPA01 Volltext |
| Summary: | Using bad data -- Putting data before theory -- Worshiping math -- Worshiping computers -- Torturing data -- Fooling yourself -- Confusing correlation with causation -- Being surprised by regression toward the mean -- Doing harm -- Case study : the Great Recession. Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. 'The 9 Pitfalls of Data Science' shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | 1 Online-Ressource (v, 256 Seiten) illustrations (black and white) |
| ISBN: | 9780191879937 |
| DOI: | 10.1093/oso/9780198844396.001.0001 |
Staff View
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| spelling | Smith, Gary 1945- Verfasser (DE-588)133231879 aut The 9 pitfalls of data science Gary Smith and Jay Cordes Nine pitfalls of data science First edition Oxford Oxford University Press 2019 1 Online-Ressource (v, 256 Seiten) illustrations (black and white) txt rdacontent c rdamedia cr rdacarrier Oxford scholarship online Includes bibliographical references and index Using bad data -- Putting data before theory -- Worshiping math -- Worshiping computers -- Torturing data -- Fooling yourself -- Confusing correlation with causation -- Being surprised by regression toward the mean -- Doing harm -- Case study : the Great Recession. Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. 'The 9 Pitfalls of Data Science' shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession Data Science (DE-588)1140936166 gnd rswk-swf Big data Quantitative research Electronic books Data Science (DE-588)1140936166 s DE-604 Cordes, Jay Verfasser (DE-588)1206898852 aut Erscheint auch als Druck-Ausgabe 978-0-19-884439-6 https://doi.org/10.1093/oso/9780198844396.001.0001 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Smith, Gary 1945- Cordes, Jay The 9 pitfalls of data science Data Science (DE-588)1140936166 gnd |
| subject_GND | (DE-588)1140936166 |
| title | The 9 pitfalls of data science |
| title_alt | Nine pitfalls of data science |
| title_auth | The 9 pitfalls of data science |
| title_exact_search | The 9 pitfalls of data science |
| title_exact_search_txtP | The 9 pitfalls of data science |
| title_full | The 9 pitfalls of data science Gary Smith and Jay Cordes |
| title_fullStr | The 9 pitfalls of data science Gary Smith and Jay Cordes |
| title_full_unstemmed | The 9 pitfalls of data science Gary Smith and Jay Cordes |
| title_short | The 9 pitfalls of data science |
| title_sort | the 9 pitfalls of data science |
| topic | Data Science (DE-588)1140936166 gnd |
| topic_facet | Data Science |
| url | https://doi.org/10.1093/oso/9780198844396.001.0001 |
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