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Language:
The course will be given in Italian. Slides and Textbooks are mainly in English -
Textbooks:Slides and handouts for students not attending the course will be available from the professor
James, Witten, Hastie, Tibshirani (2013) An Introduction to Statistical Learning (with Applications in R), Springer-Verlag
Hastie, Tibshirani, Friedman (2009) The elements of statistical learning: data mining, inference and prediction. 2nd edition, Springer-Verlag
Wickham (2016) ggplot2. Elegant Graphics for Data Analysis. 2nd Edition, Springer-Verlag
Maindonald, Braun (2010) Data Analysis and Graphics Using R: An Example-Based Approach . 3rd edition, Cambridge University Press
For Italian reading we suggest
James, Witten, Hastie, Tibshirani (2020) Introduzione all'apprendimento statistico (con applicazioni in R),
Azzalini, Scarpa (2004) Analisi dei dati e data mining, Springer-Verlag -
Learning objectives:The course provides knowledge about the analysis of data in the business environment. The course aims to provide the student with the tools to
extract relevant information from large amounts of data, with particular attention to statistical learning (statistical learning) both in a predictive
and non-supervised context (supervised and non-supervised learning).
LEARNING OUTCOMES
The course aims at completing student's training with notions and tools useful to deepen the aspects of statistical analysis in the business
environment. The training will then be completed and enriched by the following skills:
Knowledge and understanding / Applying knowledge and understanding
- Knowledge of statistical concepts for multivariate analysis and related specialized terminology
- Ability to apply the principles of statistical reasoning in the preparation and interpretation of company reports
- Ability to use R software for statistical analysis
Making judgements
- To learn the logical and statistical concepts that are indispensable for working independently in the research, selection and processing of
company data and using official statistical sources.
Communication skills
- Learn the terminology and statistical techniques of multivariate analysis to communicate or correctly discuss the results of the analysis of
company data and business reports -
Prerequisite:Mathematics (calculus), linear algebra and statistical inference (estimation and statistical test)
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Teaching methods:Frontal lectures as well as practical exercises with the use of the software
R -
Exam type:The exam is divided into a 90-minute written test (exercises in R and open questions with predefined space, to verify the knowledge of the theoretical part of the
topics covered in class) and in an oral presentation (optional) of a report prepared for the analysis of two different data sets using the R software.
In the examination, the optional test gives the opportunity of adding from 1 to 3 grades to the final grade in the cas the student reached a sufficient grade in the written exam. The final grade will be given by the sum of the written exam (in thirtieths) and the grade concerning the report, in the case the latter has been carried out. -
Sostenibilità:
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Further information:E-mail: luigi.ippoliti@unich.it
Students will be received on Tuesday between 15:00 and 17:00.
Appointments can be fixed by e-mail.
The following topics are considered as important parts of the teaching program for the fulfilment of the objectives: introduction to Data Mining
and Statistical Learning, data visualization, regression and classification, Non-supervised learning (principal component analysis and clustering)
The course aims to introduce methods and models to extract relevant information from large amounts of data, with particular attention to
statistical learning (statistical learning) both in a predictive and nonpredictive context (supervised and non-supervised learning). In order to
provide the skills for the analysis and modeling of real data, the lessons will be supplemented by R exercises in the computer room.
Program:
Introduction to data mining and statistical learning.
Data visualization techniques
Regression and Classification: multiple linear regression, discriminant analysis and K-nearest
neighbors.
Non-linear methods (flexible regression): polynomial regression and generalized additive models.
Unsupervised learning: association rules, principal component analysis, grouping methods (hierarchical Clustering and mixtures).
Scopri cosa vuol dire essere dell'Ud'A
SEDE DI CHIETI
Via dei Vestini,31
Centralino 0871.3551
SEDE DI PESCARA
Viale Pindaro,42
Centralino 085.45371
email: info@unich.it
PEC: ateneo@pec.unich.it
Partita IVA 01335970693
