Fundamentals of Computer Science
A.Y. 2019/2020
Learning objectives
The course aims at providing the students with the basics related to the computer and information science. In particular: i) formal definition of information; ii) semiotic models of communication; iii) database modelling; iv) network architecture (client - server); v) internet and world wide web: protocols and standard; vi) markup languages (html). Starting from the basics, the course presents advanced topics such as information retrieval and graph theory to analyze the digital communication and advertising. Finally, AI is introduced specifying its scope and applications so to framing the current debate on the related topics.
Expected learning outcomes
Formal definition of information
Communication Model (Shannon Weaver)
ER model and relational model
Relational algebra and SQL
Client server architecture
TCP-IP and HTTP protocols
Basics of HTML
Adserving workflow
Basics of web analytics
Basics of Information Retrieval
Graph Theory
Search engine
Social Network Analysis
AI and Machine Learning basics
Communication Model (Shannon Weaver)
ER model and relational model
Relational algebra and SQL
Client server architecture
TCP-IP and HTTP protocols
Basics of HTML
Adserving workflow
Basics of web analytics
Basics of Information Retrieval
Graph Theory
Search engine
Social Network Analysis
AI and Machine Learning basics
Lesson period: First semester
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
First semester
Course syllabus
The course aims at providing the students with the basics related to the computer and information science.
In particular:
i) formal definition of information;
ii) semiotic models of communication;
iii) database modelling;
iv) network architecture (client - server);
v) internet and world wide web: protocols and standards;
vi) markup languages (html).
Starting from the basics, the course presents advanced topics such as information retrieval and graph theory to analyze the digital communication and advertising.
Finally, AI is introduced specifying its scope and applications so to framing the current debate on the related topics.
In particular:
i) formal definition of information;
ii) semiotic models of communication;
iii) database modelling;
iv) network architecture (client - server);
v) internet and world wide web: protocols and standards;
vi) markup languages (html).
Starting from the basics, the course presents advanced topics such as information retrieval and graph theory to analyze the digital communication and advertising.
Finally, AI is introduced specifying its scope and applications so to framing the current debate on the related topics.
Prerequisites for admission
No prerequisites for admission.
Teaching methods
The course is taught by frontal lessons and seminars on information retrieval, digital advertising platform and social network analysis.
Optional exercises are proposed to attending students.
Optional exercises are proposed to attending students.
Teaching Resources
Attending Students
· S. Castano, A. Ferrara, S. Montanelli, Informazione, conoscenza e Web per le scienze umanistiche, Pearson, Milano 2009, chap. 1 to 10
· Lecture notes
Non-attending students
· S. Castano, A. Ferrara, S. Montanelli, Informazione, conoscenza e Web per le scienze umanistiche, Pearson, Milano 2009
· Lecture Notes
· http://www.cs.cornell.edu/home/kleinber/networks-book/ chap1, 2, 13, 14
· S. Castano, A. Ferrara, S. Montanelli, Informazione, conoscenza e Web per le scienze umanistiche, Pearson, Milano 2009, chap. 1 to 10
· Lecture notes
Non-attending students
· S. Castano, A. Ferrara, S. Montanelli, Informazione, conoscenza e Web per le scienze umanistiche, Pearson, Milano 2009
· Lecture Notes
· http://www.cs.cornell.edu/home/kleinber/networks-book/ chap1, 2, 13, 14
Assessment methods and Criteria
The assessement methods is an oral test.
The exercises eventually performed by attending students, are evaluated and grants two extra-points on final grade if the final assessement is positive.
Assessement criteria are assimilating basic concepts, lexical competence, ability to think concepts in scenarios.
Assessments are marked out of thirty.
The exercises eventually performed by attending students, are evaluated and grants two extra-points on final grade if the final assessement is positive.
Assessement criteria are assimilating basic concepts, lexical competence, ability to think concepts in scenarios.
Assessments are marked out of thirty.
Unita' didattica A
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Unita' didattica B
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours