CdSDATA SCIENCE AND BUSINESS INFORMATICS
Codice500PP
CFU6
PeriodoSecondo semestre
Moduli  Settore  Tipo  Ore  Docente/i  
STATISTICAL METHODS FOR DATA SCIENCE  SECSS/01  LEZIONI  48 

The student who completes successfully the course will have a solid knowledge on the main concepts and tools of statistical analysis, including the definition of a statistical model, the inference of its parameters with confidence intervals, the use of hypothesis testing. with specific applications to problems and models useful in data science. Finally the student will be able to use the language R for performing statistical analyses.
The student who completes successfully the course will have a solid knowledge on the main concepts and tools of statistical analysis, including the definition of a statistical model, the inference of its parameters with confidence intervals, the use of hypothesis testing. with specific applications to problems and models useful in data science. Finally the student will be able to use the language R for performing statistical analyses.
The student will be assessed on his/her demonstrated ability to discuss the main course contents using the appropriate terminology, and to apply the main statistical methods in different contexts.
The student will be assessed on his/her demonstrated ability to discuss the main course contents using the appropriate terminology, and to apply the main statistical methods in different contexts.
The student will be able to understand the main concept of statistical analysis and to choose and apply the appropriate tool to the case under study. The student will also be able to use the language R for performing statistical analyses.
The student will be able to understand the main concept of statistical analysis and to choose and apply the appropriate tool to the case under study. The student will also be able to use the language R for performing statistical analyses.
Attending students will do a group project on the statistical analysis of a large dataset, for which a series of questions will be proposed. The project will assess skills in the choice and use of existing statistical models.
Attending students will do a group project on the statistical analysis of a large dataset, for which a series of questions will be proposed. The project will assess skills in the choice and use of existing statistical models.
Students will be able to evaluate bias in statistical models, particularly in the case of models affecting socially sensitive decision making.
Students will be able to evaluate bias in statistical models, particularly in the case of models affecting socially sensitive decision making.
Group project and oral exams will include questions about bias in statistical models.
Group project and oral exams will include questions about bias in statistical models.
Basic knowledge of calculus. Basic knowledge of probability might be useful even if not indispensable.
Delivery: face to face
Learning activities:
 attending lectures
 participation in discussions
 individual study
 group project
Attendance: strongly advised
Teaching methods:
 Lectures
 Lab sessions in R
Delivery: face to face
Learning activities:
 attending lectures
 participation in discussions
 individual study
 group project
Attendance: strongly advised
Teaching methods:
 Lectures
 Lab sessions in R
The program covers the basic methodologies, techniques and tools of statistical analysis. This includes basic knowledge of probability theory, random variables, convergence theorems, statistical models, estimation theory, and hypothesis testing. Other topics covered include bootstrap, expectationmaximization, and applications to data science problems. Finally the program covers the use of the language R for statistical analysis.
The program covers the basic methodologies, techniques and tools of statistical analysis. This includes basic knowledge of probability theory, random variables, convergence theorems, statistical models, estimation theory, and hypothesis testing. Other topics covered include bootstrap, expectationmaximization, and applications to data science problems. Finally the program covers the use of the language R for statistical analysis.
 F.M. Dekking C. Kraaikamp, H.P. Lopuha, L.E. Meester. A Modern Introduction to Probability and Statistics. Springer, 2005.
 P. Dalgaard. Introductory Statistics with R. 2nd edition, Springer, 2008.
 F.M. Dekking C. Kraaikamp, H.P. Lopuha, L.E. Meester. A Modern Introduction to Probability and Statistics. Springer, 2005.
 P. Dalgaard. Introductory Statistics with R. 2nd edition, Springer, 2008.
Nonattending students cannot do the project. All the rest remains unchanged.
Nonattending students cannot do the project. All the rest remains unchanged.
The exam consists of a written part and an oral part. The written part lasts 2 hours and it includes open questions and exercises (both theoretical and in R). Each exercise is assigned a grade. Students are admitted to the oral part if the sum of grade is at least 18/30. The oral part consists of open questions on the topics of the course. Attending students may replace the written part with a project to be done in groups throughout the course.
Online exams: during the COVID19 restrictions, the written part and the oral part will be online. For the written part, students will connect to Google Meet (room code: 500PP) and will activate both microphone and webcam. Each sheet will include name, surname, student id, and it will be signed. A picture of the sheets will be delivered to ruggieri@di.unipi.it.
The exam consists of a written part and an oral part. The written part lasts 2 hours and it includes open questions and exercises (both theoretical and in R). Each exercise is assigned a grade. Students are admitted to the oral part if the sum of grade is at least 18/30. The oral part consists of open questions on the topics of the course. Attending students may replace the written part with a project to be done in groups throughout the course.
Online exams: during the COVID19 restrictions, the written part and the oral part will be online. For the written part, students will connect to Google Meet (room code: 500PP) and will activate both microphone and webcam. Each sheet will include name, surname, student id, and it will be signed. A picture of the sheets will be delivered to ruggieri@di.unipi.it.