Abstract :
[en] If "data science" is a recent expression, one of its parents, experimental statistics, lies back to the early 20th century. And from the beginning, it has been deeply linked to cereal science, thanks to the work of Ronald A. Fisher at the Rothamsted Experimental Station where he developed some of the fundamentals of experimental designs and data analyses, still widely used in every field of sciences, like the maximum likelihood principle, and the analysis of variance.
As the time passed, new challenges arose. With the industrialisation came the need for optimisation of the processes, standardisation and quality control of the products, ... ; the digital revolution introduced real time monitoring in the fields and in the factories, massification of the data collection, access to aerial imagery, ...; and the -omics breakthrough recently opened the pandora box of the billions of jigsaw pieces which build each living individual. Each of these (r)evolution came with their own questions, their own new problems to solve, their own data to process, resulting in new advances in the toolbox of data analysis methods.
We'll take a quick trip through time and processes to illustrate how, from crop fields to baker's shop, now more then ever, data science is everywhere.
