Sensory

Chapter 1: Dealing with quantitative perception

This chapter deals with continuous attributes. We introduce the notions of position and dispersion parameters, the concepts of inferential versus descriptive, the comparisons of two means, the notion of model, the analysis of variance, PCA, MFA...

1From sensory data collection to a collection of sensory data

2From a data set to a data frame

3From location parameters to the notion of distribution and its visualization

4From distribution to dispersion

5From dispersion to standard deviation

6From distribution to conditional distribution

7From conditional distribution to the comparison of two means

8From the comparison of two means to the notion of model

9From the notion of model to the analysis of variance model

10From the analysis of variance to the notion of distance

11From the notion of distance to the notion of inertia

12From the notion of inertia to its decomposition

13From the inertia decomposition to Principal Components Analysis and beyond

14The CheatR corner

what image shows

About this course

'Sensory Data Science' is an innovative course about data science applied to sensory data. In this course, we will try to find the right balance between statistics, computer science, mathematics, formalism, intuition, with a pinch of sensory, consumer, perception data. The spirit of the course is as follows: start with data, explore them and introduce the statistical and computer elements necessary to obtain knowledge. Data mining will be done using R software.

About us

The course was created by Sébastien Lê and implemented by Sébastien Lê, Guillaume Béguec, and Marion Moussay, from the statistics and computer science department, Institut Agro Rennes-Angers, CNRS, IRMAR - UMR 6625, F-35000 Rennes, France