Statistical methods for data science

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

Timetable: 

https://www.di.unipi.it/en/education/mds/timetable-mds

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. and some basic knowledge of the statistics of linear time series. Finally the student will 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.

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 basic knowledge of time series analysis. Finally the program covers the use of the language R for statistical analysis.
Zircon - This is a contributing Drupal Theme
Design by WeebPal.