First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
STATISTICS: CASE STUDIES
SCP4063364, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
STATISTICAL SCIENCES
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2019/20
N0
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICS: CASE STUDIES
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-laurea-magistrale
Department of reference Department of Statistical Sciences
E-Learning website https://elearning.unipd.it/stat/course/view.php?idnumber=2019-SS1736-000ZZ-2018-SCP4063364-N0
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge GUIDO MASAROTTO SECS-S/01
Other lecturers GIOVANNA CAPIZZI SECS-S/01
GIOVANNA MENARDI SECS-S/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 9.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 9.0 64 161.0 No turn

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
5 Commissione a.a.2019/20 01/10/2019 30/09/2020 MASAROTTO GUIDO (Presidente)
CAPIZZI GIOVANNA (Membro Effettivo)
MENARDI GIOVANNA (Membro Effettivo)
SCARPA BRUNO (Membro Effettivo)

Syllabus
Prerequisites: Compulsory:
Probability Theory
Statistics (Advanced)

Substantive (in addition):
Statistical Models for Social Data
Data mining
Target skills and knowledge: This course aims to foster the operational abilities of students in the statistical analysis of a variety of data.
Examination methods: The final exam consists of three parts:
- global involvement in classroom activities (both quantitative and qualitative)
- teamwork analysis of a complex data set with final presentation of a written report
- individual practical assessment (analysis of an assigned data set).
Assessment criteria: - involvement in the classroom activities (both quantitative and qualitative)
- ability to analyze a complex data set and to summarize the findings in a written report
- capability of analyzing a data set under time constraints
Course unit contents: This course aims to foster the operational abilities of students in the statistical analysis of a variety of data. Lectures will alternate (i) the presentation and critical appraisal of problems encountered in routine data analysis and (ii) student's independent/group work.
The emphasis is on the discussion and analysis of real life applications. These are used to illustrate the concepts and motivate the application of the statistical toolbox acquired by the students in previous courses for the purpose of developing their operational capabilities. As to this, the course features two moments. It starts with the course instructors presenting the practical problems and data sets together with the most suitable analysis techniques. Later on, the students will be asked to tackle independently a number of real life applications proposed by the instructors and to analyze the assigned data sets using the statistical techniques they consider to fit the best.

NB: This course is characterized by a strong interaction between students and course instructors. Students will hence be asked to sign up beforehand (the details will be announced in December 2019); admission may be limited. Beware that only second year Master's students in Statistical Sciences who passed both, Probability Theory and Statistics (Advanced), are eligible. Admission will depend on the marks achieved in the latter two exams, and of the two courses Statistical Models for Social Data and Data Mining.
Planned learning activities and teaching methods: Presentation and critical discussion of the problems encountered in real life data analysis by means of interactive computer lab sessions
Additional notes about suggested reading: All course material will be available on the e-learning platform.
Textbooks (and optional supplementary readings)