| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Science | ||
| DATA SCIENCE | ||
Course unit
STATISTICAL LEARNING 1 (MOD. A)
SCP7079227, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2017/18
STATISTICAL LEARNING 1 (MOD. A)
SCP7079227, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2017/18
Information on the course unit
| Degree course |
Second cycle degree in DATA SCIENCE SC2377, Degree course structure A.Y. 2017/18, A.Y. 2017/18 |
 bring this page with you |
|---|---|---|
| Number of ECTS credits allocated | 6.0 | |
| Type of assessment | Mark | |
| Course unit English denomination | STATISTICAL LEARNING 1 (MOD. A) | |
| Website of the academic structure | http://datascience.scienze.unipd.it/2017/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 | MONICA CHIOGNA | SECS-S/01 |
|---|
Integrated course for this unit
| Course unit code | Course unit name | Teacher in charge |
|---|---|---|
| SCP7079226 | STATISTICAL LEARNING (C.I.) | -- |
ECTS: details
| Type | Scientific-Disciplinary Sector | Credits allocated | |
|---|---|---|---|
| Core courses | SECS-S/01 | Statistics | 6.0 |
Mode of delivery (when and how)
| Period | Annual |
|---|---|
| Year | 1st Year |
| Teaching method | frontal |
Organisation of didactics
| Type of hours | Credits | Hours of teaching |
Hours of Individual study |
Shifts |
|---|---|---|---|---|
| Lecture | 6.0 | 48 | 102.0 | No turn |
Common characteristics of the Integrated Course unit
| 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 |
Specific characteristics of the Module
| Course unit contents: | Part 1: Modes of Inference
- Data: summary statistics, displaying distributions; exploring relationships - Likelihood: the likelihood, likelihood for several parameters - Estimation: maximum likelihood estimation; accuracy of estimation; the sampling distribution of an estimator; the bootstrap - Hypothesis testing - Other approaches to inference |
| Planned learning activities and teaching methods: | Lectures and Laboratories |
| Additional notes about suggested reading: | Applications can be found in:
- Nolan, D.A. & Speed, T. (2000). Stat Labs: Mathematical Statistics Through Applications. Springer. - Torgo, L. (2011). Data Mining with R: Learning with Case Studies. Chapman & Hall/CRC. Methods for specific fields of applications can be found in the following books: -Campbell, R.C. (1989). Statistics for Biologists (3rd ed.). Cambridge University Press. -Devore, J.L. (2000). Probability and Statistics for Engineering and the Sciences (5th ed.). Duxbury Press, Pacific Grove, CA. -Agresti, A. & Finlay. B. (2007). Statistical Methods for the Social Sciences (4th ed.). Prentice Hall |
| Textbooks (and optional supplementary readings) |
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