Luigi Di Biasi | STATISTICS AND DATA ANALYSIS

cod. 0522500094

STATISTICS AND DATA ANALYSIS

	0522500094
	COMPUTER SCIENCE
	EQF7
	COMPUTER SCIENCE
	2023/2024

CURRICULUM DATA SCIENCE & MACHINE LEARNING

	OBBLIGATORIO
	YEAR OF COURSE 1
	YEAR OF DIDACTIC SYSTEM 2016
	AUTUMN SEMESTER

TEACHING UNITS

	SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
	INF/01	9	72	LESSONS	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS

PROFESSORS

	STEFANO CIRILLO T Curriculum
	LUIGI DI BIASI Curriculum

	Objectives
	KNOWLEDGE AND UNDERSTANDING • DEVELOPMENT OF METHODS AND TECHNIQUES FOR THE TREATMENT AND ANALYSIS OF DATA USING ONE OF THE MOST POWERFUL AND FLEXIBLE STATISTICAL SOFTWARE, I.E. THE R PROGRAMMING LANGUAGE • DESCRIPTIVE AND INFERENTIAL STATISTICS WITH R APPLYING KNOWLEDGE AND UNDERSTANDING • APPLICATIVE PROBLEMS RELATED TO THE PROCESSING AND ANALYSIS OF STATISTICAL DATA • DEVELOPMENT OF COMPUTER APPLICATIONS FOR THE MANAGEMENT, MANIPULATION AND ANALYSIS OF STATISTICAL DATA

KNOWLEDGE AND UNDERSTANDING
• DEVELOPMENT OF METHODS AND TECHNIQUES FOR THE TREATMENT AND ANALYSIS OF DATA USING ONE OF THE MOST POWERFUL AND FLEXIBLE STATISTICAL SOFTWARE, I.E. THE R PROGRAMMING LANGUAGE
• DESCRIPTIVE AND INFERENTIAL STATISTICS WITH R

APPLYING KNOWLEDGE AND UNDERSTANDING
• APPLICATIVE PROBLEMS RELATED TO THE PROCESSING AND ANALYSIS OF STATISTICAL DATA
• DEVELOPMENT OF COMPUTER APPLICATIONS FOR THE MANAGEMENT, MANIPULATION AND ANALYSIS OF STATISTICAL DATA

	Prerequisites
	BASIC KNOWLEDGE OF PROBABILITY AND STATISTICS.

	Contents
	•THE INTEGRATED ENVIRONMENT R: INTRODUCTION AND HISTORICAL NOTES. (LESSONS, 2H) •VECTORS, ARRAYS AND MATRICES. LISTS. DATA FRAME. FACTORS. DEFINITION OF NEW FUNCTIONS. (LESSONS, 4H) •TABLES AND GRAPHS: SIMPLE FREQUENCY DISTRIBUTIONS. DOUBLE FREQUENCY DISTRIBUTIONS. CONDITIONED FREQUENCY DISTRIBUTIONS. THE MAIN GRAPHICAL REPRESENTATIONS. GRAPHIC FUNCTIONS AT A HIGH LEVEL, LOW LEVEL AND INTERACTIVE GRAPHICS. BAR CHARTS, PIE CHARTS AND STICKS. HISTOGRAMS. BOXPLOT. PARETO DIAGRAM. GRAPHICAL REPRESENTATIONS OF TABLES. GRAPHICAL REPRESENTATIONS TO COMPARE VARIABLES. SCATTERPLOT. GRAPHS OF FUNCTIONS. (LESSONS, 6H) •UNIVARIATE DESCRIPTIVE STATISTICS WITH R: INTRODUCTION TO DESCRIPTIVE STATISTICS. EMPIRICAL DISCRETE AND CONTINUOUS DISTRIBUTION FUNCTION. POSITION AND DISPERSION INDICES. SAMPLE MEAN, SAMPLE MEDIAN AND SAMPLE MODE. PERCENTILES AND QUARTILES. SAMPLE VARIANCE, SAMPLE STANDARD DEVIATION AND COEFFICIENT OF VARIATION. THE FORM OF A FREQUENCY DISTRIBUTION. SKEWNESS AND KURTOSIS. WEIGHTED AVERAGE. (LESSONS,8H) •BIVARIATE DESCRIPTIVE STATISTICS WITH R CORRELATION, COVARIANCE AND CORRELATION COEFFICIENT. LINEAR AND NONLINEAR REGRESSION MODELS. RESIDUES AND DETERMINATION COEFFICIENT. (LESSONS,8H) •TECHNIQUES OF MULTIVARIATE STATISTICAL ANALYSIS WITH R. CLUSTER ANALYSIS. INTRODUCTION TO THE ANALYSIS OF THE CLUSTER. BASICS AND DEFINITIONS. FUNCTIONS OF DISTANCE AND SIMILARITY MEASURES. OPTIMIZATION METHODS. HIERARCHICAL METHODS. ANALYSIS OF THE DENDROGRAM. NON-HIERARCHICAL METHODS. SYNTHESIS MEASURES ASSOCIATED WITH CLUSTERS. (LESSONS, 8H) •INTRODUCTION TO STATISTICAL INFERENCE. (LESSONS, 2H) •DISCRETE RANDOM VARIABLES WITH R: DISCRETE PROBABILITY DISTRIBUTIONS AND THEIR SIMULATION (BERNOULLI, BINOMIAL, GEOMETRIC, MODIFIED GEOMETRIC, NEGATIVE BINOMIAL, MODIFIED NEGATIVE BINOMIAL, HYPERGEOMETRIC, POISSON). SOME IMPORTANT RESULTS RELATED TO THE DISCRETE RANDOM VARIABLES ANALYZED WITH THE SIMULATION IN R. (LESSONS, 5H) •CONTINUOUS RANDOM VARIABLES WITH R: CONTINUOUS PROBABILITY DISTRIBUTIONS AND THEIR SIMULATION (UNIFORM, EXPONENTIAL, NORMAL, CHI-SQUARE, STUDENT). SOME IMPORTANT RESULTS RELATED TO THE CONTINUOUS RANDOM VARIABLES ANALYZED WITH THE SIMULATION IN R. (LESSONS, 5H) •STATISTICAL INFERENCE WITH R: POINT ESTIMATION. PROPERTIES OF ESTIMATORS. METHODS FOR THE SEARCH OF ESTIMATORS. METHODS OF MOMENTS AND OF THE MAXIMUM LIKELIHOOD. (LESSONS, 4H) •INTERVAL ESTIMATION WITH R: CONFIDENCE INTERVALS. CONFIDENCE INTERVALS FOR THE MEAN AND THE VARIANCE OF A NORMAL POPULATION. (LESSONS, 6H) •INTERVAL ESTIMATION FOR LARGE SAMPLES. CONFIDENCE INTERVAL FOR THE PARAMETER OF A POPULATION OF BERNOULLI, POISSON AND EXPONENTIAL. MEAN DIFFERENCES IN NORMAL POPULATIONS. MEAN DIFFERENCES IN BERNOULLI POPULATIONS. (LESSONS, 6H) •HYPOTHESIS TESTING WITH R: TESTS CONCERNING MEANS. TEST CONCERNING DIFFERENCES BETWEEN MEANS. TEST CONCERNING VARIANCE. TEST CONCERNING PROPORTIONS. (LESSONS, 4H) •GOODNESS OF FIT. HYPOTHESIS TESTING IN LINEAR AND NONLINEAR REGRESSION MODELS. (LESSONS, 4H)

Contents

•THE INTEGRATED ENVIRONMENT R: INTRODUCTION AND HISTORICAL NOTES. (LESSONS, 2H)

•VECTORS, ARRAYS AND MATRICES. LISTS. DATA FRAME. FACTORS. DEFINITION OF NEW FUNCTIONS. (LESSONS, 4H)

•TABLES AND GRAPHS: SIMPLE FREQUENCY DISTRIBUTIONS. DOUBLE FREQUENCY DISTRIBUTIONS. CONDITIONED FREQUENCY DISTRIBUTIONS. THE MAIN GRAPHICAL REPRESENTATIONS. GRAPHIC FUNCTIONS AT A HIGH LEVEL, LOW LEVEL AND INTERACTIVE GRAPHICS. BAR CHARTS, PIE CHARTS AND STICKS. HISTOGRAMS. BOXPLOT. PARETO DIAGRAM. GRAPHICAL REPRESENTATIONS OF TABLES. GRAPHICAL REPRESENTATIONS TO COMPARE VARIABLES. SCATTERPLOT. GRAPHS OF FUNCTIONS. (LESSONS, 6H)

•UNIVARIATE DESCRIPTIVE STATISTICS WITH R: INTRODUCTION TO DESCRIPTIVE STATISTICS. EMPIRICAL DISCRETE AND CONTINUOUS DISTRIBUTION FUNCTION. POSITION AND DISPERSION INDICES. SAMPLE MEAN, SAMPLE MEDIAN AND SAMPLE MODE. PERCENTILES AND QUARTILES. SAMPLE VARIANCE, SAMPLE STANDARD DEVIATION AND COEFFICIENT OF VARIATION. THE FORM OF A FREQUENCY DISTRIBUTION. SKEWNESS AND KURTOSIS. WEIGHTED AVERAGE. (LESSONS,8H)

•BIVARIATE DESCRIPTIVE STATISTICS WITH R
CORRELATION, COVARIANCE AND CORRELATION COEFFICIENT. LINEAR AND NONLINEAR REGRESSION MODELS. RESIDUES AND DETERMINATION COEFFICIENT. (LESSONS,8H)

•TECHNIQUES OF MULTIVARIATE STATISTICAL ANALYSIS WITH R. CLUSTER ANALYSIS. INTRODUCTION TO THE ANALYSIS OF THE CLUSTER. BASICS AND DEFINITIONS. FUNCTIONS OF DISTANCE AND SIMILARITY MEASURES. OPTIMIZATION METHODS. HIERARCHICAL METHODS. ANALYSIS OF THE DENDROGRAM. NON-HIERARCHICAL METHODS.
SYNTHESIS MEASURES ASSOCIATED WITH CLUSTERS. (LESSONS, 8H)

•INTRODUCTION TO STATISTICAL INFERENCE. (LESSONS, 2H)

•DISCRETE RANDOM VARIABLES WITH R: DISCRETE PROBABILITY DISTRIBUTIONS AND THEIR SIMULATION (BERNOULLI, BINOMIAL, GEOMETRIC, MODIFIED GEOMETRIC, NEGATIVE BINOMIAL, MODIFIED NEGATIVE BINOMIAL, HYPERGEOMETRIC, POISSON). SOME IMPORTANT RESULTS RELATED TO THE DISCRETE RANDOM VARIABLES ANALYZED WITH THE SIMULATION IN R. (LESSONS, 5H)

•CONTINUOUS RANDOM VARIABLES WITH R:
CONTINUOUS PROBABILITY DISTRIBUTIONS AND THEIR SIMULATION (UNIFORM, EXPONENTIAL, NORMAL, CHI-SQUARE, STUDENT). SOME IMPORTANT RESULTS RELATED TO THE CONTINUOUS RANDOM VARIABLES ANALYZED WITH THE SIMULATION IN R. (LESSONS, 5H)

•STATISTICAL INFERENCE WITH R: POINT ESTIMATION. PROPERTIES OF ESTIMATORS. METHODS FOR THE SEARCH OF ESTIMATORS. METHODS OF MOMENTS AND OF THE MAXIMUM LIKELIHOOD. (LESSONS, 4H)

•INTERVAL ESTIMATION WITH R: CONFIDENCE INTERVALS. CONFIDENCE INTERVALS FOR THE MEAN AND THE VARIANCE OF A NORMAL POPULATION. (LESSONS, 6H)

•INTERVAL ESTIMATION FOR LARGE SAMPLES. CONFIDENCE INTERVAL FOR THE PARAMETER OF A POPULATION OF BERNOULLI, POISSON AND EXPONENTIAL. MEAN DIFFERENCES IN NORMAL POPULATIONS. MEAN DIFFERENCES IN BERNOULLI POPULATIONS. (LESSONS, 6H)

•HYPOTHESIS TESTING WITH R: TESTS CONCERNING MEANS. TEST CONCERNING DIFFERENCES BETWEEN MEANS. TEST CONCERNING VARIANCE. TEST CONCERNING PROPORTIONS. (LESSONS, 4H)

•GOODNESS OF FIT. HYPOTHESIS TESTING IN LINEAR AND NONLINEAR REGRESSION MODELS. (LESSONS, 4H)

	Teaching Methods
	THE TEACHING METHOD INCLUDES THEORETICAL LESSONS INTEGRATED BY EXERCISES AND PROBLEMS, ALL RELATED TO THE METHODOLOGIES FOR THE ANALYSIS OF UNIVARIATE AND MULTIVARIATE DATA (CFUS 9, DURATA(H): 72). THE CLASS ATTENDANCE IS STRONGLY RECOMMENDED. THE STUDENTS ARE GUIDED TO LEARN, IN A CRITICAL AND RESPONSIBLE WAY, EVERYTHING WHAT THE TEACHER PRESENTS DURING THE LECTURES. STUDENTS ARE THUS ENCOURAGED TO COMMUNICATE TO THE ENTIRE CLASS THE IDEAS OF DEVELOPMENT AND OF PROBLEM SOLVING, AND ARE ALSO ENCOURAGED TO ACQUIRE SKILLS AND EXPERTISE IN MANAGING THE COMPLEXITY OF NEW PROBLEMS CONCERNING TO DATA ANALYSIS.

Teaching Methods

THE TEACHING METHOD INCLUDES THEORETICAL LESSONS INTEGRATED BY EXERCISES AND PROBLEMS, ALL RELATED TO THE METHODOLOGIES FOR THE ANALYSIS OF UNIVARIATE AND MULTIVARIATE DATA (CFUS 9, DURATA(H): 72). THE CLASS ATTENDANCE IS STRONGLY RECOMMENDED. THE STUDENTS ARE GUIDED TO LEARN, IN A CRITICAL AND RESPONSIBLE WAY, EVERYTHING WHAT THE TEACHER PRESENTS DURING THE LECTURES. STUDENTS ARE THUS ENCOURAGED TO COMMUNICATE TO THE ENTIRE CLASS THE IDEAS OF DEVELOPMENT AND OF PROBLEM SOLVING, AND ARE ALSO ENCOURAGED TO ACQUIRE SKILLS AND EXPERTISE IN MANAGING THE COMPLEXITY OF NEW PROBLEMS CONCERNING TO DATA ANALYSIS.

	Verification of learning
	THE EXAM CONSISTS OF A PROJECT AND AN ORAL TEST. THE VOTE WILL DEPEND ON THE KNOWLEDGE AND ON THE ABILITY TO APPLY THE METHODS TO SOLVE CONCRETE PROBLEMS.

	Texts
	• MICHAEL J. CRAWLEY (2017) THE R BOOK, WILEY • JANE M. HORGAN (2009) PROBABILITY WITH R. AN INTRODUCTION WITH COMPUTER SCIENCE APPLICATIONS. WILEY • ALVIN C. RENCHER (2012) METHODS OF MULTIVARIATE ANALYSIS. WILEY SERIES IN PROBABILITY AND STATISTICS • LECTURE NOTES OF THE TEACHER (IN ITALIAN)

	More Information
	TO HELP STUDENTS IN THEIR STUDY ACTIVITY, THE TEACHER WILL PROVIDE NOTES OF THE LECTURES (IN ITALIAN), THAT INCLUDE THE TOPICS AND PROBLEMS ADDRESSED. STUDENTS WHO HAVE ATTENDED ASSIDUOUSLY HAVE AN ADVANTAGE IN THE ORAL DISCUSSION BECAUSE, DURING THE LESSONS, THEY HAVE BEEN STIMULATED TO LEARN AND TO CONNECT IN A SYSTEMATIC MANNER THE VARIOUS TOPICS, AS WELL AS TO MANAGE THE COMPLEXITY OF NEW PROBLEMS.

More Information

TO HELP STUDENTS IN THEIR STUDY ACTIVITY, THE TEACHER WILL PROVIDE NOTES OF THE LECTURES (IN ITALIAN), THAT INCLUDE THE TOPICS AND PROBLEMS ADDRESSED. STUDENTS WHO HAVE ATTENDED ASSIDUOUSLY HAVE AN ADVANTAGE IN THE ORAL DISCUSSION BECAUSE, DURING THE LESSONS, THEY HAVE BEEN STIMULATED TO LEARN AND TO CONNECT IN A SYSTEMATIC MANNER THE VARIOUS TOPICS, AS WELL AS TO MANAGE THE COMPLEXITY OF NEW PROBLEMS.

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Luigi Di Biasi | STATISTICS AND DATA ANALYSIS