Course Details for A.Y. 2019/2020

The information on this page refer to the academic year 2019/2020. Therefore, several links are not available and data may be incomplete. To read the current information on the course, if it is still available, go to the university course catalogue . To read information relating other academic years, use the list at the bottom of this page.

A. Year:

2019/2020

Name:

Statistics Lab / Statistics Lab

Type:

Module

Main course:

Statistics / Statistics

Basic information

Code:

DT0366

Sector:

MAT/06

Credits:

: Master Degree in Applied Data Science 6 CFU (c)

Term:

1^st semester

Degree(s):

Master Degree in Applied Data Science 1^st anno curriculum Data for Smart City Compulsory
Master Degree in Applied Data Science 1^st anno curriculum Data for Life Science Compulsory

Language:

English

Teacher(s):

Fabio Antonelli

Course Objectives

The students, by attending this course, should acquire a basic knowledge in statistics, to be able to understand the basic theoretical tools in data management. Moreover, they should be able to solve simple theoretical statistical problems, involving the use of probability and statistics. They should also be able to tackle real simple statistical problems and employ the correct theoretical and software tools to solve them. Finally, the students should acquire sufficient skills to proceed towards a more advanced course in statistics, econometrics or in statistical learning.

Course Content

Using Statistics to summarize data sets: representation of datasets, sample mean, variance, median, covariance and correlation.
Review of fundamentals of probability: combinatorics, uniform probability spaces, independence and conditioning, main discrete and continuous random variables, marginal and joint distributions, Mean, variance, moments, covariance and correlation index. The Gaussian densities, the Law of Large Numbers, the Central Limit Theorem.
Distribution of sampling statistics, estimators, confidence intervals.
Linear regression: univariate and multivariate.
Principal component analysis
RE software to calculate statistics figures

Learning Outcomes (Dublin Descriptors)

On successful completion of this course, the student should

• Have acquired a basic knowledge of the main tools in statistical analysis. • Have a theoretical familiarity with multivariate analysis. • Be able to understand and employ the basic techniques to manage data sets. • Have acquired a basic knowledge of a statistical software such as R. • Be able to proceed towards a more advanced course in statistics, econometrics and statistical learning.

Prerequisites and Learning Activities

It is required a basic knowledge in • Basic Mathematics • Linear Algebra • Calculus Also, a basic knowledge of Probability is strongly advised.

Assessment Methods and Criteria

Written exam, with possible development of a small project with the aid of a statistical software.

Textbooks

S. M. Ross, Introductory Statistics , Academic Press.
R. A. Johnson, D.W: Wichern , Applied Multivariate Statistical Analysis (6th Edition) , Pearson.

Course page updates

This course page is available (with possible updates) also for the following academic years:

To read the current information on this course, if it is still available, go to the university course catalogue .

Course information last updated on: 10 settembre 2018, 00:35