- Docente: Pier Giovanni Bissiri
- Credits: 6
- SSD: SECS-S/01
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Law and Economics (cod. 5913)
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from Sep 20, 2022 to Oct 25, 2022
Learning outcomes
The aim of the course is to deliver skills related to usage of data analysis tools and tecniques both in descriptive and inferential statistics. At the end of the course the student will be able to use for basic tasks one of the most common data analysis softwares. Moreover, the student will know and will be able to critically apply the main tools for descriptive and inferential statistics for both the univariate and the two or more populations case. The lab activity is aimed at improving autonomy of the students about data management.
Course contents
Introduction to R and RStudio.
Arithmetics, mathematics and logic in R. Data structures in R.
Creation and management of variables and dataframes. Data importing.
Descriptive analysis of data and graphical representations.
Statistical inference for the mean of a gaussian population and for a proportion.
Comparison of means of two population.
Linear regression.Readings/Bibliography
The following books are freely available on the internet.
Wickham, Hadley, and Grolemund, Garrett. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Stati Uniti, O'Reilly Media, 2016. https://r4ds.had.co.nz
Måns Thulin, Modern statistics with R, 2021. http://modernstatisticswithr.com/
The following book is available in bookshops:
Alan Agresti, Maria Kateri, Foundations of Statistics for Data Scientists with R and Python, Taylor & Francis, 2021
Teaching methods
Class lectures.
Each student will need to bring his/her own laptop after installing R and RStudio in this order:
install R from https://www.cran.r-project.org/
install RStudio from https://www.rstudio.org/download/desktop
In view of the type of activities and teaching methods adopted, the attendance of this training activity requires the prior participation of all students in Modules 1 and 2 of safety training in the workplace, in e-learning mode.
Assessment methods
The exam will be a practical test of data analysis in a computer laboratory.
Grade reject
The grade can be rejected by the student only once. To reject the grade, the student must send an email to piergiovanni.bissiri@unibo.it by the specified date.
Grading policy
insufficient <18; sufficient 18-23; good 24-27; very good 28-30; excellent 30 cum laude.
Teaching tools
- material provided by the lecturer on virtuale.unibo.it
- statistical software R www.r-project.org
- integrated development environment RStudio www.rstudio.com
Students with disability or specific learning disabilities (DSA) are required to make their condition known to find the best possibile accomodation to their needs.
Office hours
See the website of Pier Giovanni Bissiri
SDGs
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.