B0925 - Data Science Applications

Learning outcomes

By the end of the course the student will develop advanced expertise in analysing complex real-world data. In particular the student will be able to: - identify and apply appropriate statistical techniques to real application problems; - implement the various stages of advanced statistical analysis; - work in a team to develop a data analysis project; -present results of analyses in a short talk and/or poster and demonstrate effective communication skills.

Course contents

During this course the students will work on data science applications related to specific disciplines such as biology, epidemiology, genetics, engineering, finance, and the social sciences.

Specifically, this course will focus on modeling two types of data structured in an unusual way, that are network data and text data, together with their specific tools and methodologies to analyze and interpret the corresponding results

Readings/Bibliography

Materials will be provided by the teacher in Virtuale.

Teaching methods

"Students attending labs, due to research or teaching activities, are considered in terms of safety as staff members of the universities and thus have to attain specific online certifications provided at the link below."

Link

 

Theoretical and practical lectures at the lab.

Assessment methods

The course will be assessed by the reports that the groups write and submit on the case studies during the course. Students who cannot give a presentation at the exam dates will be examined orally on flexible individually agreed dates. In any case, it will not be possible to read during the presentation from a phone or written notes: it has to be a proper oral discourse of the project.

The course is not marked quantitatively, but as "idoneo/non idoneo": it will not be possible, at any stage, to convert this "Pass/Fail" to a numerica value, not even after having passed the exam. Please, do not consider this course if you need for any reason a proper quantitative scoring.

AI policy: if any AI tool is used in the preparation of the presentation or the report itself, all the relevant prompt have to be submitted inside the report.

 

Studenti/sse con DSA o disabilità temporanee o permanenti: si raccomanda di contattare per tempo l’ufficio di Ateneo responsabile (https://site.unibo.it/studenti-con-disabilita-e-dsa/it ). Sarà sua cura proporre agli/lle studenti/sse interessati/e eventuali adattamenti, che dovranno comunque essere sottoposti, con un anticipo di 15 giorni, all’approvazione del/della docente, che ne valuterà l'opportunità anche in relazione agli obiettivi formativi dell'insegnamento.

Teaching tools

Supporting material to be posted on Virtuale.

The course makes use of the R statistical system. Basic knowledge of R is required.

Office hours

See the website of Saverio Ranciati