Scheda programma d'esame
STATISTICAL METHODS FOR DATA SCIENCE
DANIELE TANTARI
Codice500PP
CFU6
PeriodoSecondo semestre
LinguaItaliano

ModuliSettoreTipoOreDocente/i
STATISTICAL METHODS FOR DATA SCIENCESECS-S/01LEZIONI48
 SALVATORE RUGGIERI unimap DANIELE TANTARI unimap
Programma non disponibile nella lingua selezionata
Learning outcomes
Knowledge

The student who completes successfully the course will have a solid knowledge on the main concepts and tools of statistical analysis, including the definition of a statistical model, the inference of its parameters with confidence intervals, the use of hypothesis testing. and some basic knowledge of the statistics of linear time series. Finally the student will be able to use the language R for performing statistical analyses.

Assessment criteria of knowledge

The student will be assessed on his/her demonstrated ability to discuss the main course contents using the appropriate terminology, and to apply the main statistical methods in different contexts. Written exam consists of a 2 hours test. Oral exam consists of a discussion of the written exam, and open questions on the topics of the course.

Methods:

• Final oral exam
• Final written exam

The students taking the exam in the first sessions have the option of preparing a small research project in place of the written exam.

Skills

The student will be able to understand the main concept of statistical analysis and to choose and apply the appropriate tool to the case under study. The student will also be able to use the language R for performing statistical analyses.

Assessment criteria of skills

Attending students will do a group project on the statistical analysis of a large dataset, for which a series of questions will be proposed. The project will assess skills in the choice and use of existing statistical tests.

Prerequisites

Basic knowledge of calculus. Basic knowledge of probability might be useful even if not indispensable.

Teaching methods

Delivery: face to face

Learning activities:

• attending lectures
• participation in discussions
• individual study
• group project

Teaching methods:

• Lectures
• Lab sessions

Syllabus

The program covers the basic methodologies, techniques and tools of statistical analysis. This includes basic knowledge of probability theory, random variables, convergence theorems, statistical models, estimation theory, and hypothesis testing. Other topics covered include bootstrap, expectation-maximization, and basic knowledge of time series analysis. Finally the program covers the use of the language R for statistical analysis.

Bibliography
•  F.M. Dekking C. Kraaikamp, H.P. Lopuha, L.E. Meester. A Modern Introduction to Probability and Statistics. Springer, 2005.
• P. Dalgaard. Introductory Statistics with R. 2nd edition, Springer, 2008.

Non-attending students info

Non-attending students cannot do the project. All the rest remains unchanged.

Assessment methods

The exam consists of a written part and an oral part. The written part includes open questions and exercises. The oral part consists of open questions on the topics of the course. Attending students may replace the written part with a project to be done in groups throughout the course.

Ultimo aggiornamento 20/02/2018 11:43