Lecture material will be posted on the Moodle class web page.
Information on manuals that use R for Data Science will be provided during the course.
Learning Objectives
The objective of the course is to provide basic knowledge and skills in Data Science. The lab sessions with R aim to apply some of the methods presented in the lessons and highlight the operational problems connected with applications on data.
Prerequisites
It is assumed that students are familiar with basic descriptive and inferential statistics (topics covered in B018993-STATISTICA).
Teaching Methods
Classroom lectures and lab sessions.
Further information
Students must send an e-mail request to the teacher from their institutional UNIFI address to access the Moodle class web page.
Type of Assessment
The exam will be oral. The questions will cover the whole program. The following are assessed: the level of understanding of the various topics, the presentation skills and the mastery of the technical language related to the context of interest.
The exam also requires you to explain the content of one or more scripts (without the comments) run during the lab sessions and to comment on the results. An example of an R script without the comments is available on the Moodle page.
Course program
Introduction to data science. Big data. Data wrangling and exploratory data analysis. Data display. Classification of methodologies. Some examples of supervised and unsupervised methods. Introduction to R and use of R for Data Science.