Provided by:
UNIPI
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Salvatore RUGGIERI
Semester:
2
Hours:
48
Exam:
Y
Educational Goals:
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.
Prerequisites:
Basic knowledge of calculus. Basic knowledge of probability might be useful even if not indispensable.
Programme:
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, expectation-maximization, and applications to data science problems. Finally the program covers the use of the language R for statistical analysis.
