DATA MANAGEMENT FOR COMMUNICATION | Università degli studi di Bergamo

DATA MANAGEMENT FOR COMMUNICATION

Attività formativa monodisciplinare
Codice dell'attività formativa: 
149010-ENG

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2020/2021
Insegnamento (nome in italiano): 
DATA MANAGEMENT FOR COMMUNICATION
Insegnamento (nome in inglese): 
DATA MANAGEMENT FOR COMMUNICATION
Tipo di attività formativa: 
Altra attività formativa
Tipo di insegnamento: 
Obbligatoria
Settore disciplinare: 
STATISTICA (SECS-S/01)
Anno di corso: 
1
Anno accademico di offerta: 
2020/2021
Crediti: 
6
Responsabile della didattica: 

Altre informazioni sull'insegnamento

Modalità di erogazione: 
Didattica Convenzionale
Lingua: 
Inglese
Ciclo: 
Secondo Semestre
Obbligo di frequenza: 
No
Ore di attività frontale: 
48
Ore di studio individuale: 
102
Ambito: 
Altre conoscenze utili per l'inserimento nel mondo del lavoro
Prerequisites

There are no prerequisites but it is advisable to know the basic topics about probability theory and statistical inference. Probability theory: casual experiments, probability measure, probability spaces, random variables, Normal distribution, T-Student distribution, Chi-squared distribution, F distribution, use of the qunatiles of those distributions. Statistical Inference: parametric statistical models, random samples statistics, sample distributions, distribution of the sample mean and sample variance for Normal population, point estimate, confidence intervals, testing hypotheses, simple linear regression model.

Educational goals

The course contributes to the educational objectives of the course of study, in particular with reference to the tools of statistical analysis for control and management of data and their communication in the economic-financial areas and social fields.
The course offers the methodological basis for understanding and being able to represent and analyze the different and new types of data and the relationships between them.
At the end of the course the student will be able to choose the most suitable statistical device to synthesize, extract the most salient information and describe both the simplest and the most complex phenomena. They will be able to communicate the results taking into consideration only the aspects of the phenomenon that are important to analyze and formalize. They will also have a good knowledge of Data Science techniques with the R program.

Course content

Common data structures.
Exploratory Data Analysis:
data Visualisation, the grammar of graphics; data transformation, covariation; patterns and models, visualising models, formula and model families.
Data Modeling: basic and multiple regression; sampling, bostrapping and confidence intervals, hypothesis testing. Statistical Networks. Data Story Telling.

Teaching methods

Frontal lectures with numerous examples and discussion of case studies. Labs in classroom with active participation of students.

Assessment and Evaluation

The assessment of learning takes place through the oral discussion of a final report that the students must return one week before the exam date.
The report is assigned to a group of three before the end of the course. It consists of a case study in which the procedures learned in the course have to be applied to produce a report that describes the problem, shows the solutions identified by the group and describes the techniques used to overcome the issues and summarize the results. During the oral discussion, the teacher will be able to determine the contribution of each member of the group to the final report. The final grade will be an equally weighted average between this contribution and the overall grade given to the report.

Further information

If the course will be done remotely or blended, changes will be possibly made compared to what is stated in the syllabus to make the course and exams accessible also in these forms.