First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
INFORMATION ENGINEERING
Course unit
STATISTICAL DATA ANALYSIS (Numerosita' canale 1)
INL1000178, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
INFORMATION ENGINEERING
IN0513, Degree course structure A.Y. 2011/12, A.Y. 2018/19
N2cn1
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL DATA ANALYSIS
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN0513-000ZZ-2017-INL1000178-N2CN1
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 LORENZO VANGELISTA ING-INF/03

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INL1000178 STATISTICAL DATA ANALYSIS (Numerosita' canale 1) LORENZO VANGELISTA IN0507
INL1000178 STATISTICAL DATA ANALYSIS (Numerosita' canale 1) LORENZO VANGELISTA IN0512

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/04 Automatics 5.0
Core courses ING-INF/03 Telecommunications 4.0

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

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2011 course timetable

Examination board
Board From To Members of the board
23 A.A. 2018/2019 01/10/2018 15/03/2020 VANGELISTA LORENZO (Presidente)
FINESSO LORENZO (Membro Effettivo)
ZANELLA ANDREA (Membro Effettivo)
BADIA LEONARDO (Supplente)
CALVAGNO GIANCARLO (Supplente)
CORVAJA ROBERTO (Supplente)
ERSEGHE TOMASO (Supplente)
LAURENTI NICOLA (Supplente)
MILANI SIMONE (Supplente)
ROSSI MICHELE (Supplente)
TOMASIN STEFANO (Supplente)
ZANUTTIGH PIETRO (Supplente)
ZORZI MICHELE (Supplente)
22 A.A. 2018/2019 01/10/2018 15/03/2020 FINESSO LORENZO (Presidente)
VANGELISTA LORENZO (Membro Effettivo)
CALVAGNO GIANCARLO (Supplente)
21 A.A. 2017/2018 01/10/2017 15/03/2019 VANGELISTA LORENZO (Presidente)
FINESSO LORENZO (Membro Effettivo)
ZANELLA ANDREA (Membro Effettivo)
BADIA LEONARDO (Supplente)
CALVAGNO GIANCARLO (Supplente)
CORVAJA ROBERTO (Supplente)
ERSEGHE TOMASO (Supplente)
LAURENTI NICOLA (Supplente)
MILANI SIMONE (Supplente)
ROSSI MICHELE (Supplente)
TOMASIN STEFANO (Supplente)
ZANUTTIGH PIETRO (Supplente)
ZORZI MICHELE (Supplente)
20 A.A. 2017/2018 01/10/2017 15/03/2019 FINESSO LORENZO (Presidente)
VANGELISTA LORENZO (Membro Effettivo)
CALVAGNO GIANCARLO (Supplente)

Syllabus
Prerequisites: Analisi matematica 1, Analisi matematica 2, Algebra lineare e geometria.
Target skills and knowledge: Basic knowledge of probability theory, random variables and random processes. Upon completion of the course students should be able to build simple probabilistic models and to compute the relevant probabilities.
Examination methods: Witten, closed book.
Assessment criteria: Students must demonstrate that they have acquired the basic knowledge of probability theory, discrete and continuous random variables and the fundamentals of random processes. Also they will have to demonstrate the ability to apply the theory to find appropriate probabilistic models related to random phenomena and to know how to solve problems of calculation of probabilities.
Course unit contents: Probability:
Probability spaces and their properties. Elements of combinatorics and classic probability problems. Conditional probability. Independent events.

Random Variables (RV):
Definition of RV. Discrete RV, definition, examples, density and distribution functions. Special discrete RV: Bernoulli, Binomial, Geometric and Poisson. Absolutely continuous (a.c.) RV, definition, examples. Special a.c. RV: Uniform, Exponential, Gaussian. Transformations of RV. Expected value, moments and their properties. Moment generating function and characteristic function. Gaussian RV. Markov and Chebychev inequalities.

Random Vectors (RVe):
Definition of Rve. Joint distribution and its properties. Continuous RVe. Joint density and its properties. Discrete RVe. Joint probability density and its properties. Expected value of RVe and moments RVe. Characteristic function of a RVe. Random variables uncorrelated and independent. Gaussian RVe. Sum of independent RV.

Sequences of Random Variables:
Sequences of RV. Convergence in distribution, in probability, on average. Law of large numbers and central limit theorem.

Random processes:
Definitions. Complete probabilistic description and power description. Stationariety. Correlation and spectral density. Spectral analysis of filtered random processes.
Planned learning activities and teaching methods: Class lessons. During the lessons the theoretical aspects of the course are exposed and application examples and exercises are carried out. Additional individual exercises as homework with subsequent illustration of the solutions are proposed.
Additional notes about suggested reading: All course topics are discussed in the classroom. The lecture notes can be integrated from the textbook and additional material made available on the Moodle platform.
Textbooks (and optional supplementary readings)
  • L.Finesso, Lezioni di Probabilit√†. Padova: Libreria Progetto, 2018. Cerca nel catalogo
  • Gubner, John A., Probability and random processes for electrical and computer engineersJohn A. Gubner. Cambridge [etc.]: Cambridge University press, 2006. Cerca nel catalogo