Università degli Studi di Napoli "Parthenope"

Teaching schedule

Academic year: 
2019/2020
Belonging course: 
Disciplinary sector: 
ECONOMIC STATISTICS (SECS-S/03)
Language: 
Italian
Credits: 
6
Year of study: 
3
Teachers: 
Cycle: 
Second semester
Hours of front activity: 
48

Language

Italian

Course description

The aim of the course is to provide knowledge for the use of statisticsassupport in the management and in business processes..The courseprovides a theoretical and applicative approach in order to allow the student to have the ability to choose and or collect, process and interpretstatistical information useful to managerialdecisions.
Knowledge and understanding:
Demonstrate to understanding the usefulness of statistical information in business activities and the capacity to associate the most appropriate statistical method in relation to the fixed managerial objectives.
Applyingknowledge and understanding:Learning skills to identify a business problem and find the solutionthrough the application of appropriate statisticaltools.
Makingjudgements: Ability to develop a criticalapproach in the issuesaddressed.
Communications:Be able to expressin a clear and exhaustiveway the themesaddressed in the teaching, choosing the most appropriate way in relation to the differentstakeholders.
Lifelonglearningskills:to demonstratelearningabilities with bibliographic and othermethodsrelating to the discipline under the study.

Prerequisites

Knowledge of descriptive and inferential statistical methods; calculus.

Syllabus

I blocco di lezioni (24 ore):
Statistics in business management. Big data. Hadoop and cloud. Supervised and unsupervised approach.. Linear regression model. Decision tree. Measurement of proximity. Cluster analysis: hierarchical method and k-means algorithm.Text analysis.
II blocco di lezioni (24 ore):
Machine learning. Knime software: user’s interface, nodes, build data science workflows, loop cycle, specific nodes for big data.

Statistics in business management. Big data. Hadoop and cloud. Supervised and unsupervised approach.. Linear regression model. Decision tree. Measurement of proximity. Cluster analysis: hierarchical method and k-means algorithm.Text analysis. Machine learning. Knime software: user’s interface, nodes, build data science workflows, loop cycle, specific nodes for big data.

Teaching Methods

Lectures andapplications with activeparticipation of the students.

Textbooks

- De Mauro Andrea. Big data analytics. Analizzare e interpretare dati con il machine learning. Apogeo. 2019.
- De Luca Amedeo. Big data analytics e data mining. 2018. Apogeo.

Learning assessment

The learningassessmentisbased on moments of collectivediscussion in the classroom, and in the callsprovided by the academiccalendar, on an oral test, accompanied by a vote expressed in thirtieths, aimedatassessing the learningcapacity of the contents of the program.

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