Study plan

For further information, please check this website: http://datascience.i3s.uniroma1.it/it

Curriculum unico

First year

Orientamento unico
Course Semester CFU SSD Language
1047221 - ALGORITHMIC METHODS OF DATA MINING AND LABORATORY First semester 9 ING-INF/05 English
1047264 - FUNDAMENTALS OF DATA SCIENCE AND LABORATORY First semester 9 INF/01 English
10589600 - Statistical methods in data science and laboratory First semester 9 English
10589600 - Statistical methods in data science and laboratory Second semester 3 English
1047223 - NETWORKING FOR BIG DATA AND LABORATORY Second semester 9 ING-INF/03 English
- A SCELTA DELLO STUDENTE Second semester 6 Italian
GRUPPO OPZIONALE A Go to group
GRUPPO OPZIONALE B Go to group
GRUPPO OPZIONALE C Go to group

Second year

Orientamento unico
Course Semester CFU SSD Language
- A SCELTA DELLO STUDENTE First semester 6 Italian
AAF1149 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK Second semester 3 Italian
AAF1022 - Final exam Second semester 24 Italian
GRUPPO OPZIONALE B Go to group
GRUPPO OPZIONALE C Go to group
GRUPPO OPZIONALE D Go to group

Optional Groups

GRUPPO OPZIONALE B: The student must acquire 18 CFU from the exams below
Course Year Semester CFU SSD Language
1047197 - DATA MANAGEMENT FOR DATA SCIENCE First year Second semester 6 ING-INF/05 English
1047205 - CLOUD COMPUTING First year Second semester 6 INF/01 English
1047200 - DATA MINING TECHNOLOGY FOR BUSINESS AND SOCIETY First year Second semester 6 ING-INF/05 English
1047214 - DATA PRIVACY AND SECURITY Second year First semester 6 INF/01 English
1047202 - SIGNAL PROCESSING FOR BIG DATA Second year First semester 6 ING-INF/03 English
10589623 - Computational Data Analysis Second year First semester 6 ING-INF/05 English
1056023 - Smart Environments Second year Second semester 6 ING-INF/03 English
10589621 - Advanced Machine Learning Second year Second semester 6 INF/01 English
GRUPPO OPZIONALE C: The student must acquire 6 CFU from the exams below
Course Year Semester CFU SSD Language
1047208 - STATISTICAL LEARNING First year Second semester 6 SECS-S/01 English
1047209 - QUANTITATIVE MODELS FOR ECONOMIC ANALYSIS AND MANAGEMENT First year Second semester 6 ING-IND/35 English
1041415 - OPTIMIZATION METHODS FOR MACHINE LEARNING Second year First semester 6 MAT/09 English
1056087 - Statistics for Stochastic Processes Second year Second semester 6 SECS-S/01 English
GRUPPO OPZIONALE A: The student must acquire 6 CFU from the exams below
Course Year Semester CFU SSD Language
1047215 - INTELLECTUAL PROPERTY COMPETITION AND DATA PROTECTION LAW First year Second semester 6 IUS/04 English
1047212 - Economics of Network Industries First year Second semester 6 SECS-P/06 English
GRUPPO OPZIONALE D: The student must acquire 12 CFU from the exams below
Course Year Semester CFU SSD Language
1047220 - BIOINFORMATICS Second year First semester 6 ING-INF/06 English
1056085 - Big Data for Official Statistics Second year First semester 6 SECS-S/05 English
1047216 - DIGITAL EPIDEMIOLOGY Second year First semester 6 ING-INF/06 English
10589627 - Neural Networks for Data Science Applications Second year First semester 6 ING-IND/31 English
1047218 - EARTH OBSERVATION DATA ANALYSIS Second year Second semester 6 ING-INF/02 English
1056129 - Data Driven Economics Second year Second semester 6 SECS-P/02 English
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS Second year Second semester 6 SECS-S/03 English
10589730 - Geomatics and Geoinformation Second year Second semester 6 ICAR/06 English

Programme Regulations of the  
Master’s Degree Programme
in 
Data Science
(Master of Science in Data Science)
Class LM-91 
INFORMATION SOCIETY TECHNIQUES AND METHODS

Students’ booklet 2018/2019

Specific learning outcomes

The degree programme offers an interdisciplinary approach gathering contributions from Engineering, Computer Science, Statistics, Economic and Organizational Sciences, as well as specific knowledge of the main Data Science application domains. In particular, the Master’s degree programme in Data Science offers the necessary professional knowledge for the development of the big data collection, management, processing and analysis technologies, and the resulting translation of these into key information for the knowledge and decision-making process within innovative business and social sectors.

A course of study in Data Science must meet the scientific and technological challenges related to the use of new global platforms for data storage and processing: personal, government and commercial data and their related application leave individually-owned systems for cloud-computing and cloud-storage systems with their related reliability, privacy and security problems.  

The Master’s degree programme in Data Science aims at training new professionals who can contribute to increase the efficiency and reliability of public institutions, private companies and local administrations, with particular reference to open data and their use for the development of more efficient services for companies and citizens and for the optimization of resource management in urban contexts. The Master’s degree in Data Science also aims at training professional who can work in public and private agencies with the task of inserting big data in the processes of economic and social analysis.  

The interdisciplinary approach of the Master’s degree programme in Data Science and its rigorous methodology make it suitable for students who hold a Bachelor’s degree in all fields of Information Engineering, Computer Science, Statistics, as well as in Economics, Mathematics and Physics.

In addition to specific field knowledge, theoretical and scientific aspects are also key parts of the course catalogue, as they are necessary to describe and interpret the problems found in application contexts where Data Science innovative methodologies are developed. They are related to devising, planning, implementing and managing complex big data management and analysis systems, but also to developing testing skills and acquiring fluency in English.

The final exam, consisting of a written dissertation, is a key element of students’ education/training, as graduands can apply the knowledge and methodologies acquired in industrial, scientific, social and economic analysis fields. The final exam proves the graduand’s expertise in the area, his/her skills in working autonomously and a good communication level.  

The course of study is addressed to international students, thanks to the quality of research carried out by lecturers, and the fact that it is taught in English. Moreover, the course of study is designed so as to be closely connected to the job market through many important applied research projects carried out by lecturers in collaboration with national, international universities, companies and public administrations.

The Master’s graduate in Data Science will also be adequately trained for both basic and applied research, both in universities and research centres and in corporate R&D departments, in Italy and abroad.

The course catalogue encompasses all multidisciplinary skills offered by the 4 Departments of the Faculty of Information Engineering, Informatics and Statistics (I3S), the Department of Statistical Sciences, the “Antonio Ruberti” Department of Computer, Automatic and Management Engineering, the Department of Computer Science and the Department of Information Engineering, Electronics and Telecommunications.

The programme regulations of the Master’s degree, according to the related provisions, will define the overall number of hours available to students for personal study and other individual learning activities.  

Admission requirements and credit recognition

- Curricular requirements

The curricular requirements are the following:
(a) Bachelor’s degree or other equivalent degree obtained abroad
(b) acquisition of at least 90 credits in the whole set of the following sectors:
- Mathematical and Computer Sciences: MAT/*, INF/01
- Physical Sciences: FIS/*
- Economic and Statistical Sciences: SECS-P/*, SECS-S/*
- Industrial and Information Engineering: ING-IND/*, ING-INF/*
- Biological Sciences: BIO/*
- Juridical Sciences: IUS/*
(c) Knowledge of English language: B2 or higher.

Admission to the Master’s degree programme will be possible for students who hold a Bachelor’s Degree in the following categories: L-8 (Information Engineering), L-31 (Computer Sciences and Technologies), L-41 (Statistics), L-18 (Economics and Corporate Management Sciences), L-30 (Physical Sciences and Technologies), L-33 (Economical Sciences), L-35 (Mathematical Sciences) and the related categories referred to in the M.D. 509/1999.

- Admission

(A) Without the prerequisites indicated in (a) and (b) it is not possible to register for the Master’s Degree Programme.

(B) In the presence of prerequisites (a) and (b), students’ personal knowledge will be assessed on the basis of the results obtained with the graduate degree they submit upon applying for admission to the Master’s degree programme.  
In particular, assessment of personal knowledge will focus on the following fields:
1. Mathematics: Differential and integral calculus for functions of one or more real variables; basic notions of linear algebra and analytic geometry in the plane and in space.
2. Probability: random variables, distributions and expected values; main models for random variables; convergence for successions of random variables.
3. Computer Science: Programming principles; at least one programming language among C, C++, C#, Java, Python.
Knowledge of the above mentioned areas will be assessed by an ad-hoc committee appointed by the Educational Area Committee, who will automatically admit to the Master’s degree programme the students who will have acquired at least:

1. 12 credits in the areas MAT/03 (Geometry), MAT/05 (Mathematical Analysis)
2. 6 credits in the areas MAT/06 (Probability)
3. 6 credits in the areas INF/01 (Computer Science) or ING-INF/05 (Information processing systems)

Students who do not possess the credits indicated above will be interviewed for the assessment of the required knowledge.

(C) Students who do not hold a certificate of B2 level of English or who have not acquired at least 3 credits in English language (also eligibility), will be interviewed to assess their knowledge of the English language.

Study plan description

The degree programme includes a first set of 39 credits in key academic disciplines aimed at providing students with the basic statistical, engineering and computer knowledge needed for the development of software tools and the necessary infrastructures for the big data collection, processing and organization of the mathematical and statistical models useful for their analysis. The 39 credits will include at least 10 credits deriving from laboratory activities or individual study. These key courses are compulsory for all students. The 39 compulsory credits are divided into 27 credits on computer technologies and 12 on statistical disciplines.

Students will therefore choose up to 30 credits on key academic disciplines. At least 6 of the 36 credits must be chosen among humanities, social, juridical and economic sciences. These subjects aim at training a professional profile combining engineering and computer knowledge with statistics, management, economic and juridical skills. These skills need to be developed together with a thorough knowledge of the economic, social and organizational context where Data Science methodologies are applied.

The course of study will be completed by 12 elective credits and 12 credits deriving from activities on similar academic disciplines.

Attendance is not compulsory, except for laboratory and practical activities.

All courses are taught in English.

The knowledge acquired will be assessed through mid-term tests, discussions of group work, or students’ individual written work traditionally examined.  

Final exam

The final exam can be related to a project, research, methodological activity or internship carried out at industries, public research institutions or in the University laboratories. The final graduation exam is the submission and discussion of an original project and a written report supervised by a lecturer. The work done will show that the student masters Data Science methodologies and/or their application in a specific field, matching the demands of processes of technological innovations. The final exam will be arranged so as to be itself an important step into the job market.

Expected professional opportunities for Master’s graduates  

The professional profiles that have been identified are those of Data Scientist, Open Data Manager, Data Intelligence Professional, Big Data Infrastructure Professional. In their professional tasks, Data Science postgraduates analyse, present and predict main trends in data flows, identify the software tools needed for big data processing, coordinate open data collection and publication in public and private sectors, coordinate programming groups and devise new service types based on big data, insert data science methodologies into the corporate organizational and market strategy processes, manage the main software, hardware and network infrastructures for big data.

The main skills associated to this professional profile are the following: data statistical analysis, understanding of software infrastructures, data management, data mining, data flows and their formats, management and business analytics, planning and analysis of hardware, software and network architectures for big data, juridical and economic skills in the ICT field. Jobs will be found in large, middle-sized and small enterprises, public and local administrations, public and private research bodies, institutes for economic and social analysis.

Transfer

Students who decide to be transferred to the Master’s degree programme in Data Science will apply to the Student Affairs Office of the Faculty of Information Engineering, Informatics and Statistics (c/o University campus). Applications will follow the indications of the Student Affairs Office and will have to include the list of passed exams for which recognition is required. The Teaching Committee will establish the recognized exams and credits and will define, together with the student, his/her course of study. The latter, according to the course planning and content of the Master’s degree programme in Data Science, will take into account the previous study track.

Studies abroad

Degree courses attended in European or foreign universities which have signed agreements and projects with the Faculty of Information Engineering, Informatics and Statistics, will be recognized, according to the terms and conditions provided by such agreements.
Following permission of the Teaching Committee, students can spend a study period abroad with the Erasmus LLP exchange project. In accordance with the University Programme Regulations, in case of studies, exams and degrees obtained abroad, the Teaching Committee examines the single degree programme for the credit recognition process in the related academic disciplines.

 

General information

Courses syllabi and exam texts: degree programmes and exam texts are available on the website: http://datascience.i3s.uniroma1.it/it

 

Tutoring services: The lecturers Aris Anagnostopoulos, Pierpaolo Brutti, Antonio Cianfrani and Brunero Liseo carry out tutoring and student guidance activities, according to the timetable available in the Degree course website. Moreover, tutoring services are provided by the Faculty, compatibly with economic funds and teaching contracts.

Quality assessment: The degree programme, in collaboration with the Faculty, surveys the opinions of students attending all the degree courses. The survey system is combined with a quality pathway and the self-assessment group, lecturers, students and academic staff are in charge of it. The results of the self-assessment group’s surveys and analyses are used to improve leaning activities.

Specific learning outcomes

The Master’s degree programme in Data Science offers an interdisciplinary approach gathering contributions from Engineering, Computer Science, Statistics, Economic and Organizational Sciences, as well as specific knowledge of the main Data Science application domains. In particular, the Master’s degree programme in Data Science offers the necessary professional knowledge for the development of the big data collection, management, processing and analysis technologies, and the resulting translation of these into key information for the knowledge and decision-making process within innovative business and social sectors.

A course of study in Data Science must meet the scientific and technological challenges related to the use of new global platforms for data storage and processing: personal, government and commercial data and their related application leave individually-owned systems for cloud-computing and cloud-storage systems with their related reliability, privacy and security problems.  

The Master’s degree programme in Data Science aims at training new professionals who can contribute to increase the efficiency and reliability of public institutions, private companies and local administrations, with particular reference to open data and their use for the development of more efficient services for companies and citizens and for the optimization of resource management in urban contexts. The Master’s degree in Data Science also aims at training professional who can work in public and private agencies with the task of inserting big data in the processes of economic and social analysis.  

The interdisciplinary approach of the Master’s degree programme in Data Science and its rigorous methodology make it suitable for students who hold a Bachelor’s degree in all fields of Information Engineering, Computer Science, Statistics, as well as in Economics, Mathematics and Physics.

In addition to specific field knowledge, theoretical and scientific aspects are also key parts of the course catalogue, as they are necessary to describe and interpret the problems found in application contexts where Data Science innovative methodologies are developed. They are related to devising, planning, implementing and managing complex big data management and analysis systems, but also to developing testing skills and acquiring fluency in English.

The final exam, consisting of a written dissertation, is a key element of students’ education/training, as graduands can apply the knowledge and methodologies acquired in industrial, scientific, social and economic analysis fields. The final exam proves the graduand’s expertise in the area, his/her skills in working autonomously and a good communication level.  

The course of study is addressed to international students, thanks to the quality of research carried out by lecturers, and the fact that it is taught in English. Moreover, the course of study is designed so as to be closely connected to the job market through many important applied research projects carried out by lecturers in collaboration with national, international universities, companies and public administrations.

The Master’s graduate in Data Science will also be adequately trained for both basic and applied research, both in universities and research centres and in corporate R&D departments, in Italy and abroad.

The course catalogue encompasses all multidisciplinary skills offered by the 4 Departments of the Faculty of Information Engineering, Informatics and Statistics (I3S), the Department of Statistical Sciences, the “Antonio Ruberti” Department of Computer, Automatic and Management Engineering, the Department of Computer Science and the Department of Information Engineering, Electronics and Telecommunications.

The programme regulations of the Master’s degree, according to the related provisions, will define the overall number of hours available to students for personal study and other individual learning activities.  

Admission requirements and credit recognition

 

- Curricular requirements

The curricular requirements are the following:
(a) Bachelor’s degree or other equivalent degree obtained abroad  

(b) acquisition of at least 90 credits in the whole set of the following sectors:
- Mathematical and Computer Sciences: MAT/*, INF/01
- Physical Sciences: FIS/*
- Economic and Statistical Sciences: SECS-P/*, SECS-S/*
- Industrial and Information Engineering: ING-IND/*, ING-INF/*
- Biological Sciences: BIO/*
- Juridical Sciences: IUS/*
(c) Knowledge of English language: B2 or higher.

Admission to the Master’s degree programme will be possible for students who hold a Bachelor’s Degree in the following categories: L-8 (Information Engineering), L-31 (Computer Sciences and Technologies), L-41 (Statistics), L-18 (Economics and Corporate Management Sciences), L-30 (Physical Sciences and Technologies), L-33 (Economical Sciences), L-35 (Mathematical Sciences) and the related categories referred to in the M.D. 509/1999.

 

- Admission

(A) Without the prerequisites indicated in (a) and (b) it is not possible to register for the Master’s Degree Programme.

(B) In the presence of prerequisites (a) and (b), students’ personal knowledge will be assessed on the basis of the results obtained with the degree they submit upon applying for admission to the Master’s degree programme.  
In particular, assessment of personal knowledge will focus on the following fields:
1. Mathematics: Differential and integral calculus for functions of one or more real variables; basic notions of linear algebra and analytic geometry in the plane and in space.
2. Probability: random variables, distributions and expected values; main models for random variables; convergence for successions of random variables.
3. Computer Science: Programming principles; at least one programming language among C, C++, C#, Java, Python.
Knowledge of the above mentioned areas will be assessed by an ad-hoc committee appointed by the Educational Area Committee, who will automatically admit to the Master’s degree programme the students who will have acquired at least:

1. 12 credits in the areas MAT/03 (Geometry), MAT/05 (Mathematical Analysis)
2. 6 credits in the areas MAT/06 (Probability)
3. 6 credits in the areas INF/01 (Computer Science) or ING-INF/05 (Information processing systems)

Students who do not possess the credits indicated above will be interviewed for the assessment of the required knowledge.

(C) Students who do not hold a certificate of B2 level of English or who have not acquired at least 3 credits in English language (also eligibility), will be interviewed to assess their knowledge of the English language.

 

Degree programme description

The degree programme includes a first set of 39 credits in key academic disciplines aimed at providing students with the basic statistical, engineering and computer knowledge needed for the development of software tools and the necessary infrastructures for the big data collection, processing and organization of the mathematical and statistical models useful for their analysis. The 39 credits will include at least 10 credits deriving from laboratory activities or individual study. These key courses are compulsory for all students. The 39 compulsory credits are divided into 27 credits on computer technologies and 12 on statistical disciplines.

Students will therefore choose up to 30 credits on key academic disciplines. At least 6 of the 36 credits must be chosen among humanities, social, juridical and economic sciences. These subjects aim at training a professional profile combining engineering and computer knowledge with statistics, management, economic and juridical skills. These skills need to be developed together with a thorough knowledge of the economic, social and organizational context where Data Science methodologies are applied.

 

The course of study will be completed by 12 elective credits and 12 credits deriving from activities on similar academic disciplines.

Attendance is not compulsory, except for laboratory and practical activities.

All courses are taught in English.

The knowledge acquired will be assessed through mid-term tests, discussions of group work, or students’ individual written work traditionally examined.  

 

Final exam

The final exam can be related to a project, research, methodological activity or internship carried out at industries, public research institutions or in the University laboratories. The final graduation exam is the submission and discussion of an original project and a report supervised by a lecturer. The work done will show that the student masters Data Science methodologies and/or their application in a specific field, matching the demands of processes of technological innovations. The final exam will be arranged so as to be itself an important step into the job market.

 

Expected professional opportunities for the Master’s graduates  

The professional profiles that have been identified are those of Data Scientist, Open Data Manager, Data Intelligence Professional, Big Data Infrastructure Professional. In their professional tasks, Data Science postgraduates analyse, present and predict main trends in data flows, identify the software tools needed for big data processing, coordinate open data collection and publication in public and private sectors, coordinate programming groups and devise new service types based on big data, insert data science methodologies into the corporate organizational and market strategy processes, manage the main software, hardware and network infrastructures for big data.

The main skills associated to this professional profile are the following: data statistical analysis, understanding of software infrastructures, data management, data mining, data flows and their formats, management and business analytics, planning and analysis of hardware, software and network architectures for big data, juridical and economic skills in the ICT field. Jobs will be found in large, middle-sized and small enterprises, public and local administrations, public and private research bodies, institutes for economic and social analysis.

 

Transfer

Students who decide to be transferred to the Master’s degree programme in Data Science will apply to the Student Affairs Office of the Faculty of Information Engineering, Informatics and Statistics (c/o University campus). Applications will follow the indications of the Student Affairs Office and will have to include the list of passed exams for which recognition is required. The Teaching Committee will establish the recognized exams and credits and will define, together with the student, his/her course of study. The latter, according to the course planning and content of the Master’s degree programme in Data Science, will take into account the previous study track.

 

Studies abroad

Degree courses attended in European or foreign universities which have signed agreements and projects with the Faculty of Information Engineering, Informatics and Statistics, will be recognized, according to the terms and conditions provided by such agreements.
Following permission of the Teaching Committee, students can spend a study period abroad with the Erasmus LLP exchange project. In accordance with the University Programme Regulations, in case of studies, exams and degrees obtained abroad, the Teaching Committee examines the single degree programme for the credit recognition process in the related academic disciplines.

 

 

General information

Courses syllabi and exam texts: degree programmes and exam texts are available on the website: http://datascience.i3s.uniroma1.it/it

 

Tutoring services: The lecturers Aris Anagnostopoulos, Pierpaolo Brutti, Antonio Cianfrani and Brunero Liseo carry out tutoring and student guidance activities, according to the timetable available in the Degree course website. Moreover, tutoring services are provided by the Faculty, compatibly with economic funds and teaching contracts.

Quality assessment: The degree programme, in collaboration with the Faculty, surveys the opinions of students attending all the degree courses. The survey system is combined with a quality pathway, and the self-assessment group, lecturers, students and academic staff are in charge of it. The results of the self-assessment group’s surveys and analyses are used to improve leaning activities.