DATA MANAGEMENT FOR COMMUNICATION | Università degli studi di Bergamo - Didattica e Rubrica


Attività formativa monodisciplinare
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Attività formativa a scelta dello studente
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Didattica Convenzionale
Secondo Semestre
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A scelta dello studente

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 individually before the end of the course. It consists of a case study in which the procedures learned in the course must be applied. A paper must be submitted that describes the problem, shows the solutions identified and describes the techniques used to solve the problems, and summarize the results. During the oral discussion, the following will be verified: 1) the level of theoretical knowledge of the topics covered in the prepared report, 2) the originality of the contribution to the analysis conducted, and 3) the effectiveness of the oral presentation of the results, with particular reference to the aspects deduced from the statistical analysis presented. The final grade will be an equally weighted average between these 3 elements of the oral exam and the overall grade given to the written 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.