[ABE-L] Data Science and Statistics: different worlds?

[Doris Fontes]

Para quem não viu ainda: esse debate aconteceu em 2015, mas continua muito
atual e os debatedores são ótimos.

Data Science and Statistics: different worlds?https://youtu.be/C1zMUjHOLr4

<https://youtu.be/C1zMUjHOLr4>
<https://youtu.be/C1zMUjHOLr4>

RoyalStatSoc <https://www.youtube.com/channel/UC83oOOF9lg-g1XMT_UK1tUw>
5.32K subscribers
SUBSCRIBED
Chris Wiggins (Chief Data Scientist, New York Times) David Hand (Emeritus
Professor of Mathematics, Imperial College) Francine Bennett (Founder,
Mastodon-C) Patrick Wolfe (Professor of Statistics, UCL / Executive
Director, UCL Big Data Institute) Zoubin Ghahramani (Professor of Machine
Learning, University of Cambridge) Chair: Martin Goodson (Vice-President
Data Science, Skimlinks) Discussant: John Pullinger (UK National
Statistician) In the last few years data science has become an increasingly
popular discipline. Often linked to the use and analysis of ‘big data’,
data scientists are seen as the new professionals who can unlock the
potential of an increasingly data-rich world, and to generate economic and
social benefits from the data revolution. However within the world of
statistics, the ‘big data’ and ‘data scientist’ developments are sometimes
labelled as hypes, and ‘data science’ is seen as a rebranding of what
should be statistics. One of the often heard criticisms of big data
analytics is that there’s a lack of statistical rigour which can lead to
the wrong decisions. As with any new discipline there are questions about
exactly what data science is. Has the relevance of statistics been
diminished because of new types of data or technologies which need a
radical new approach? Is data science about ‘getting the job done’, and
statistics about the deeper scientific understanding? Are our universities
offering students the right skill sets to meet the high demand for data
scientists?

<https://youtu.be/C1zMUjHOLr4>

<https://youtu.be/C1zMUjHOLr4>

<https://youtu.be/C1zMUjHOLr4>
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