| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Science | ||
| DATA SCIENCE | ||
Course unit
STATISTICAL LEARNING (C.I.)
SCP7079226, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
STATISTICAL LEARNING (C.I.)
SCP7079226, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
Information on the course unit
| Degree course |
Second cycle degree in DATA SCIENCE SC2377, Degree course structure A.Y. 2017/18, A.Y. 2018/19 |
 bring this page with you |
|---|---|---|
| Number of ECTS credits allocated | ||
| Type of assessment | Mark | |
| Course unit English denomination | STATISTICAL LEARNING (C.I.) | |
| Website of the academic structure | http://datascience.scienze.unipd.it/2018/laurea_magistrale | |
| Department of reference | Department of Mathematics | |
| Mandatory attendance | No | |
| Language of instruction | English | |
| 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 | ALBERTO ROVERATO | SECS-S/01 |
|---|
Modules of the integrated course unit
| Course unit code | Course unit name | Teacher in charge |
|---|---|---|
| SCP7079227 | STATISTICAL LEARNING 1 (MOD. A) | ALBERTO ROVERATO |
| SCP7079228 | STATISTICAL LEARNING 2 (MOD. B) | ALBERTO ROVERATO |
Course unit organization
| Period | |
|---|---|
| Year | |
| Teaching method | frontal |
Examination board
| Board | From | To | Members of the board |
|---|---|---|---|
| 2 a.a 2018/2019 | 01/10/2018 | 30/09/2019 |
ROVERATO
ALBERTO
(Presidente)
GUOLO ANNAMARIA (Membro Effettivo) ADIMARI GIANFRANCO (Supplente) CELANT GIORGIO (Supplente) CORTESE GIULIANA (Supplente) |
| Prerequisites: | basic probability theory; multivariable calculus; linear algebra; basic computing skills |
| Target skills and knowledge: | become familiar with statistical thinking; gain adequate proficiency in the development and use of standard statistical inference tools; be able to analyse datasets using a modern programming language such as R |
| Examination methods: | written test |
| Assessment criteria: | the successful student should show knowledge of the key concepts, skills in the analysis of data and competency in applications |
- Lecturing
- Laboratory
- Problem based learning
- Case study
- Moodle (files, quizzes, workshops, ...)
- Latex
- R (free download)