Scienze Biologiche | PRINCIPLES OF INFORMATICS

cod. 0512800019

PRINCIPLES OF INFORMATICS

	0512800019
	DEPARTMENT OF CHEMISTRY AND BIOLOGY "ADOLFO ZAMBELLI"
	EQF6
	BIOLOGICAL SCIENCES
	2022/2023

PERCORSO SHARED STUDY PLAN

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

TEACHING UNITS

SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
INF/01	4	32	LESSONS	SUPPLEMENTARY COMPULSORY SUBJECTS
INF/01	2	24	EXERCISES	SUPPLEMENTARY COMPULSORY SUBJECTS

EXAMS

	Exam	Date	Session
	PRINCIPI DI INFORMATICA	26/06/2023 - 09:00	SESSIONE ORDINARIA

	Objectives
	THE AIM OF THE COURSE IS TO PROVIDE A GENERAL INTRODUCTION TO THE COMPUTER SCIENCE AND TO THE USE OF SPECIFIC TOOLS FOR THE STATISTICAL ANALYSIS OF BIOLOGICAL DATA. IN THE FIRST PART OF THE COURSE WILL BE PROVIDED SOME BASIC KNOWLEDGE ON THE ARCHITECTURE OF COMPUTERS AND ON COMPUTER NETWORKS, ON THE REPRESENTATION OF INFORMATION IN COMPUTERS, ON THE FUNDAMENTALS OF PROGRAMMING AND OF ALGORITHM DESIGN. THE AIM OF THE SECOND PART OF THE COURSE IS TO FAMILIARIZE STUDENTS WITH THE USE OF COMPUTERS FOR STATISTICAL COMPUTATION.

THE AIM OF THE COURSE IS TO PROVIDE A GENERAL INTRODUCTION TO THE COMPUTER SCIENCE AND TO THE USE OF SPECIFIC TOOLS FOR THE STATISTICAL ANALYSIS OF BIOLOGICAL DATA. IN THE FIRST PART OF THE COURSE WILL BE PROVIDED SOME BASIC KNOWLEDGE ON THE ARCHITECTURE OF COMPUTERS AND ON COMPUTER NETWORKS, ON THE REPRESENTATION OF INFORMATION IN COMPUTERS, ON THE FUNDAMENTALS OF PROGRAMMING AND OF ALGORITHM DESIGN. THE AIM OF THE SECOND PART OF THE COURSE IS TO FAMILIARIZE STUDENTS WITH THE USE OF COMPUTERS FOR STATISTICAL COMPUTATION.

	Prerequisites
	ARE NOT REQUIRED SPECIFIC PREREQUISITES: BASIC CONCEPTS OF MATHEMATICS ARE USED.

	Contents
	FIRST PART: •AN INTRODUCTION TO COMPUTER SCIENCE. COMPUTER ARCHITECTURE. COMPUTER SYSTEM ORGANIZATION. (LESSON 3H) •NUMBERING SYSTEMS. BINARY REPRESENTATION OF NUMERIC AND TEXTUAL (LESSON 6H, LAB 2H) •INFORMATION. BINARY REPRESENTATION OF SOUNDS AND IMAGES. (LESSON 2H) •BOOLEAN LOGIC AND GATES. EXAMPLES OF LOGIC CIRCUIT DESIGN AND CONSTRUCTION. (LESSON 4H, LAB 2H) •THE CONCEPT OF AN ALGORITHM. THE EFFICIENCY OF ALGORITHMS. REPRESENTING ALGORITHMS. PSEUDOCODE. (LESSON 2H, LAB 2H). •INTRODUCTION TO HIGH-LEVEL LANGUAGE PROGRAMMING. THE R LANGUAGE. (LESSON 2H, LAB 2H) •PROGRAMMING WITH R. SEQUENTIAL STRUCTURES. CONDITIONAL AND ITERATIVE STRUCTURES. EXAMPLES OF ALGORITHMIC PROBLEM SOLVING IN R. R (LESSON 3H, LAB 4H) •SELECTION SORT AND BUBBLE SORT. SEQUENTIAL SEARCH AND BINARY SEARCH. BIOLOGICAL APPLICATIONS. (LESSON 2H) SECOND PART: •DATA MANAGEMENT IN R. DATA STRUCTURES IN R. (LESSON 2H, LAB 2H) •DESCRIPTIVE ANALYSIS FOR QUANTITATIVE AND QUALITATIVE VARIABLES. SOME GRAPHICAL REPRESENTATIONS IN R. (LESSON 2H, LAB 4H) •MEAN, MEDIAN, QUANTILE AND MEASURES OF VARIABILITY. (LESSON 2H, LAB 3H) •BIVARIATE DISTRIBUTION. CORRELATION. LINEAR REGRESSION. RESIDUAL ANALYSIS IN LINEAR REGRESSION. (LESSON 2H, LAB 3H)

Contents

FIRST PART:
•AN INTRODUCTION TO COMPUTER SCIENCE. COMPUTER ARCHITECTURE. COMPUTER SYSTEM ORGANIZATION. (LESSON 3H)
•NUMBERING SYSTEMS. BINARY REPRESENTATION OF NUMERIC AND TEXTUAL (LESSON 6H, LAB 2H)
•INFORMATION. BINARY REPRESENTATION OF SOUNDS AND IMAGES. (LESSON 2H)
•BOOLEAN LOGIC AND GATES. EXAMPLES OF LOGIC CIRCUIT DESIGN AND CONSTRUCTION. (LESSON 4H, LAB 2H)
•THE CONCEPT OF AN ALGORITHM. THE EFFICIENCY OF ALGORITHMS. REPRESENTING ALGORITHMS. PSEUDOCODE. (LESSON 2H, LAB 2H).
•INTRODUCTION TO HIGH-LEVEL LANGUAGE PROGRAMMING. THE R LANGUAGE. (LESSON 2H, LAB 2H)
•PROGRAMMING WITH R. SEQUENTIAL STRUCTURES. CONDITIONAL AND ITERATIVE STRUCTURES. EXAMPLES OF ALGORITHMIC PROBLEM SOLVING IN R. R (LESSON 3H, LAB 4H)
•SELECTION SORT AND BUBBLE SORT. SEQUENTIAL SEARCH AND BINARY SEARCH. BIOLOGICAL APPLICATIONS. (LESSON 2H)

SECOND PART:
•DATA MANAGEMENT IN R. DATA STRUCTURES IN R. (LESSON 2H, LAB 2H)
•DESCRIPTIVE ANALYSIS FOR QUANTITATIVE AND QUALITATIVE VARIABLES. SOME GRAPHICAL REPRESENTATIONS IN R. (LESSON 2H, LAB 4H)
•MEAN, MEDIAN, QUANTILE AND MEASURES OF VARIABILITY. (LESSON 2H, LAB 3H)
•BIVARIATE DISTRIBUTION. CORRELATION. LINEAR REGRESSION. RESIDUAL ANALYSIS IN LINEAR REGRESSION. (LESSON 2H, LAB 3H)

	Teaching Methods
	THE TEACHING METHOD INCLUDES THEORETICAL LESSONS INTEGRATED CONTINUOUSLY BY EXERCISES AND PROGRAMMING EXAMPLES.

	Verification of learning
	THE FREQUENCY OF LECTURES IS STRONGLY RECOMMENDED. THE EXAMINATION CONSISTS OF AN ORAL TEST. THE END RESULT DEPENDS ON THE KNOWLEDGE ACQUIRED BY APPLYING THE PROGRAMMING TECHNIQUES AND THE STATISTICAL METHODS TO SOLVE CONCRETE PROBLEMS.

	Texts
	- J. GLENN BROOKSHEAR, DENNIS BRYLOW: “INFORMATICA - UNA PANORAMICA GENERALE” (13/ED.) PEARSON ITALIA, 2020. - BRUNO BERTACCINI: “INTRODUZIONE ALLA STATISTICA COMPUTAZIONALE CON R”, FIRENZE UNIVERSITY PRESS, 2018. - LECTURE NOTES OF THE TEACHER (IN ITALIAN).

	More Information
	TO HELP THE STUDENTS IN THE INDIVIDUAL STUDY, THE TEACHER WILL PROVIDE NOTES OF THE LECTURES (IN ITALIAN), THAT INCLUDE THE TOPICS AND PROBLEMS ADDRESSED.

Lessons Timetable

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