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termes, plutôt que de tester l'existence de certaines [hypothèses] au sein d'un ensemble de données, les algorithmes qui analysent des données massives ont pour but de découvrir des associations significatives entre les données, et ce sans être guidés par des hypothèses."* (Chapitre 7) + +> Kitchin, Rob. 2022. [The Data Revolution: A Critical Analysis of Big Data, Open Data & Data Infrastructures.](https://uk.sagepub.com/en-gb/eur/the-data-revolution/book269711) Second edition. Los Angeles, CA: Sage Publications. + +--- + ### Le risque : confondre corrélation et causalité Deux événements (appelons les X et Y) sont corrélés si l’on observe une relation entre les deux. Une erreur de raisonnement courante consiste à dire : « X et Y sont corrélés, donc X cause Y ». On .red[confond corrélation et causalité]. diff --git a/sections/section1.html b/sections/section1.html index 0048d09..288b460 100644 --- a/sections/section1.html +++ b/sections/section1.html @@ -198,6 +198,19 @@ Les bases de données relationnelles facilitent grandement le traitement des données puisqu'elles paraissent à travers une interface utilisateur : « il faut protéger les futurs usagers de grandes banques de données d’avoir à connaitre comment les données sont organisées dans la machine » (Codd 1970). ] +--- +### Petit exercice + +Quelle est la relation entre ces deux tables d'une même base de données ? Quel est ici l'identifiant unique ? + +![](./img/SGBD1.png) + + +--- +### Petit exercice - correction + +![](./img/SGBD2.png) + --- ### Le tableur (1979) : *data to the people* @@ -284,6 +297,17 @@ --- +### En d'autres termes + +Rob Kithin résume la position de plusieurs epistémologistes en ces termes. + + +> *"En d'autres termes, plutôt que de tester l'existence de certaines [hypothèses] au sein d'un ensemble de données, les algorithmes qui analysent des données massives ont pour but de découvrir des associations significatives entre les données, et ce sans être guidés par des hypothèses."* (Chapitre 7) + +> Kitchin, Rob. 2022. [The Data Revolution: A Critical Analysis of Big Data, Open Data & Data Infrastructures.](https://uk.sagepub.com/en-gb/eur/the-data-revolution/book269711) Second edition. Los Angeles, CA: Sage Publications. + +--- + ### Le risque : confondre corrélation et causalité Deux événements (appelons les X et Y) sont corrélés si l’on observe une relation entre les deux. Une erreur de raisonnement courante consiste à dire : « X et Y sont corrélés, donc X cause Y ». On .red[confond corrélation et causalité].