Descriptive and inferential statistics.
The aims of this course is to provide fundamental principles of multidimensional statistics for the study of complex phenomena at an exploratory non-probabilistic level, to learn the processes aimed at reducing the quantity of information by creating global synthetic indicators or multidimensional classification models and to experiment such methods in real dataset. The contents are:
-fundamentals of descriptive and inferential statistics;
-introduction to data mining: dataset, outlier detection, missing data, data transformation;
-prediction of quantitative variables: regression models and regression trees;
-methods of classification: prediction of categorical variables, discriminant analysis, classification trees and cluster analysis;
-data Analysis and Data Mining with R
At the end of the course the student will have acquired:
- A basic knowledge of statistical data analysis.
- A good knowledge to data mining
- The skills necessary to seek evidence from key suppliers of statistical data,
- Mathematical and statistical skills needed to plan and carry out a statistical data analysis;
The course will be mainly in the form of lectures but there are also practical application with R and exercises
The student, at the end of the course should be able to apply to concrete cases the conceptual tools, models and statistical techniques covered during the course.
The teaching tools used allow the immediate applicability of concepts learned. The checks will be carried out by means of an oral examination
James G., Witten D., Hastie T., Tibshirami R., An Introduction to Statistical Learning. Springer, 2021
Azzalini A., Scarpa B. Data analysis and data mining. An introduction, Oxford University Press, 2012
Fundamentals of descriptive and inferential statistics.
Introduction to data mining: dataset, outlier detection, missing data, data transformation.
Prediction of quantitative variables: regression models, regression trees.
Methods of classification: prediction of categorical variables, discriminant analysis, classification trees and cluster analysis