First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
DATA SCIENCE
Course unit
STATISTICAL LEARNING 2 (MOD. B)
SCP7079228, 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
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination STATISTICAL LEARNING 2 (MOD. B)
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

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

Calendar
Start of activities 02/10/2017
End of activities 15/06/2018

Examination board
Examination board not defined

Syllabus

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 2
- Models : normal linear models; inference for linear models; generalized linear models; inference for generalized linear models
- Model selection
- Multivariate Analysis: dimension reduction; classification; clustering
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)
  • Hastie, Trevor J.; Tibshirani, Robert, The elements of statistical learningdata mining, inference, and predictionTrevor Hastie, Robert Tibshirami, Jerome Friedman. New York: Springer, 2009. Cerca nel catalogo
  • Lavine, M., Introduction to Statistical Thought. --: None, 2013. http://people.math.umass.edu/~lavine/Book/book.html