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
1047221 - ALGORITHMIC METHODS OF DATA MINING AND LABORATORY	○ The course presents the main algorithmic techniques of data mining, necessary for data science. They offer to the student the basis for analyzing data for a variety of applications that deal with semistructured or unstructured data, such as textual data, transactions, and graph and information-network data. At the end of the course the student will have a knowledge of the main theoretical ideas of data mining, as well as some basic knowledge and experience in using programming tools for analyzing and mining data.	First semester	9	ING-INF/05
1047264 - FUNDAMENTALS OF DATA SCIENCE AND LABORATORY	Learning from data in order to gain useful predictions and insights. At the end of the course students will have an understanding of the basic programming skills needed for data analysis and visualization. They will also have familiarity of the typical data processing workflow of data preparation and scraping, visualization and exploratory analysis and final statistical modeling. Students will become familiar with the main Python libraries for data science.	First semester	9	INF/01
10589600 - Statistical methods in data science and laboratory	Learning goals Statistical Methods in Data Science is a two-semester course aimed at providing the fundamental tools for: setting up probabilistic models; understanding the basic principles of the main inferential problems: estimation, hypothesis testing, model checking and forecasting; understanding and contrasting the two main inferential paradigms, namely frequentist and Bayesian statistics; implementing inference on observed data through both optimization and simulation-based (approximation) techniques such as: Bootstrap Monte Carlo Monte Carlo Markov Chain (MCMC) understanding comparative merits of alternative strategies developing statistical computations within a suitable software environment like R (www.r-project.org), OpenBUGS (http://openbugs.net/w/FrontPage) and STAN (http://mc-stan.org/). Knowledge and understanding On successful completion of this course, students will: know the main statistical principles, inferential problems, paradigms and algorithms; assess the empirical and theoretical performance of different modeling approaches; know the main platforms, programming languages to develop effective implementations. Applying knowledge and understanding Besides the understanding of theoretical aspects, thanks to applied homeworks and a dedicated laboratory in the second semester focused on Bayesian modeling, students will be constantly challenged to use and evaluate all the techniques they have learned as well as to propose new modelization suitable for specific tasks at hand. Making judgements On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical methodologies and results. Communication skills In preparing the report and oral presentation for the final project of the second semester laboratory, students will learn how to effectively communicate information, ideas, problems and solutions to specialists but also to a general audience. Learning skills In this course the students will develop the skills necessary for a successful understanding and application of new statistical methodologies together with their effective implementation. The goal is of course to grow an active attitude towards continued learning throughout a professional career.	First semester	9
Statistical methods in data science and laboratory II		First semester	9	SECS-S/01
10589600 - Statistical methods in data science and laboratory	Learning goals Statistical Methods in Data Science is a two-semester course aimed at providing the fundamental tools for: setting up probabilistic models; understanding the basic principles of the main inferential problems: estimation, hypothesis testing, model checking and forecasting; understanding and contrasting the two main inferential paradigms, namely frequentist and Bayesian statistics; implementing inference on observed data through both optimization and simulation-based (approximation) techniques such as: Bootstrap Monte Carlo Monte Carlo Markov Chain (MCMC) understanding comparative merits of alternative strategies developing statistical computations within a suitable software environment like R (www.r-project.org), OpenBUGS (http://openbugs.net/w/FrontPage) and STAN (http://mc-stan.org/). Knowledge and understanding On successful completion of this course, students will: know the main statistical principles, inferential problems, paradigms and algorithms; assess the empirical and theoretical performance of different modeling approaches; know the main platforms, programming languages to develop effective implementations. Applying knowledge and understanding Besides the understanding of theoretical aspects, thanks to applied homeworks and a dedicated laboratory in the second semester focused on Bayesian modeling, students will be constantly challenged to use and evaluate all the techniques they have learned as well as to propose new modelization suitable for specific tasks at hand. Making judgements On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical methodologies and results. Communication skills In preparing the report and oral presentation for the final project of the second semester laboratory, students will learn how to effectively communicate information, ideas, problems and solutions to specialists but also to a general audience. Learning skills In this course the students will develop the skills necessary for a successful understanding and application of new statistical methodologies together with their effective implementation. The goal is of course to grow an active attitude towards continued learning throughout a professional career.	Second semester	3
Statistical methods in data science and laboratory I		Second semester	3	SECS-S/01
1047223 - NETWORKING FOR BIG DATA AND LABORATORY	General Objectives: These classes aim at providing the students with a comprehensive understanding of networking principles and current networking technologies at an introductory level. The focus in on the Internet evolution for big data support and the cloud networking with special attention to networking solution for data centers. A preliminary introductory part of the course is defined to equalize the background of potentially heterogeneous classes and to unify networking concepts, terms and technical language. The course also provides a laboratory activity based on the use of a network emulator and a packet sniffer. Specific Objectives: Knowledge and understanding: the student must know the principles of fundamental network protocols used in an IP network. Applying knowledge and understanding: the student must be able to apply the networking principles to realize a functioning emulated network and to analyze the traffic of a real network. Making judgements: the student must be able to critically detect the drawbacks of classical networking solutions when applied in a data center scenario Learning skills: the student must be able to follow advanced course on networking topics	Second semester	9	ING-INF/03
- A SCELTA DELLO STUDENTE		Second semester	6
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
- A SCELTA DELLO STUDENTE		First semester	6
AAF1149 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.	Second semester	3
AAF1022 - Final exam	The student will present and discuss the results of a technical activity, producing a written thesis supervised by a professor and showing the ability to master the methodologies of data science	Second semester	24
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
1047197 - DATA MANAGEMENT FOR DATA SCIENCE	The main goal of the course is to present the basic concepts of data management systems. The first part of the course introduces the main aspects of relational database systems, including basic functionalities, file and index organizations, and query processing. The second part of the course aims at presenting the main non-relational approaches to data management, in particular, multidimensional data management, large-scale data management, and open data management.	First year	Second semester	6	ING-INF/05
1047205 - CLOUD COMPUTING	General Objectives: The purpose of the course is to give students the basic concepts of distributed systems and then to focus on cloud computing technologies. The course cover theoretical and practical aspects with a focus on real examples. At the end of the course students are supposed to be capable to chose, setup and use cloud services and to design and deploy scalable architectures and elastic applications. Specific Objectives: Knowledge and understanding: On completion of the course, the student will be be able to describe and to explain - the general concepts related to distributed systems - the concepts of system and application virtualization - the mechanisms and algorithms used in cloud computing - the technologies for cloud storage - the big data processing frameworks - the cyber security issues and solutions in cloud computing Applying knowledge and understanding: On completion of the course, the student will be able: - to design and to implement a scalable architecture and to deploy an elastic application - to write and to present practical results in the form of technical report - to analyze and to present scientific work - to select, to configure and to run cloud services by using management GUI and API offered by IaaS providers - to design and to configure elastic infrastructure and to deploy elastic applications. - to make design choices that account for cyber security issues Making judgements: On completion of the course, the student will: - be capable to assess and to compare cloud technologies and cloud services, as well as big data processing frameworks - be capable to identify, to assess and to compare state of the art solutions - strengthen his/her critical thinking ability Communication skills: On completion of the course, the student will: - be capable to discuss on and to convey his/her own opinion on cloud technologies - be capable to present the analysis of a selected topic to a wide audience Learning skills: During the course, the student will develop and will enhance his/her critical thinking skill by means of studying and analyzing scientific work and technical documentation. Moreover, the student will improve his/her capability to integrate information from different sources, e.g. books, technical/scientific papers, practical experiences.	First year	Second semester	6	INF/01
1047200 - DATA MINING TECHNOLOGY FOR BUSINESS AND SOCIETY	The course will present fundamental technologies for advanced data mining applications. The course will start with presenting the methodologies for storing and retrieving information on the Web, mining application logs, mining social media, collaborative filtering and personalization. The course will also present the basic technlogies for mining geo-spatial data and data produced from large-scale sensing environments. Applications will include mining of consumer preferences, computational advertising, online marketplaces, digital marketing and mining data in physical spaces. As part of the course students will carry on a field study on a relevant use case for a selected application.	First year	Second semester	6	ING-INF/05
1047214 - DATA PRIVACY AND SECURITY	General Objectives Ensuring the privacy of personal data, and securing the computing infrastructures, are key concerns when collecting and analyzing sensitive data sets. Example of these data sets include medical data, personal communication, personal and company-wide financial information. The course is meant to cover an overview of modern techniques aimed at protecting data privacy and security in such applications. Specific Objectives The students will learn the basic cryptographic techniques and their application to obtaining privacy of data in several applications, including cloud computing, statistical databases, distributed computation, and cryptocurrencies. Knowledge and Understanding -) Modern cryptographic techniques and their limitations. -) Techniques for achieving privacy in statistical databases. -) Techniques for designing cryptographic currencies and distributed ledgers. -) Techniques for secure distributed multiparty computation. Applying knowledge and understanding: -) How to select the right cryptographic scheme for a particular application. -) How to design a differentially private mechanism. -) How to program a secure cryptosystem, or a secure smart contract, or a secure cryptographic protocol. Autonomy of Judgment The students will be able to judge the security of the main cryptographic applications. Communication Skills How to describe the security of cryptographic standards, privacy-preserving statistical databases, and blockchains. Next Study Abilities The students interested in research will learn what are the main open challenges in the area, and will obtain the necessary background for a deeper study of the subjects.	Second year	First semester	6	INF/01
1047202 - SIGNAL PROCESSING FOR BIG DATA	This is a methodological course whose objectives are to make the students able to represent information distributed over a graph and to solve complex optimization problems in distributed form. The problems concern the extraction of information from data distributed across various machines and to make inferences about networked data. These goals are achieved by first teaching the theoretical foundations necessary to formulate and solve the above problems and then inviting the students to write Matlab programs implementing the algorithms solving problems of interest.	Second year	First semester	6	ING-INF/03
10589623 - Computational Data Analysis	The challenges of data analysis have increasingly focussed on scalability issues. Algorithms that used to be regarded as efficient are no longer viable. Since then algorithmic research has begun to address these issues, developing new models of computation and design paradigms. In this course, we will focus on the design and analysis of a number of these algorithms. Important topics include probabilistic algorithms for data analysis such as clustering and low rank approximation, sublinear-time, and dynamic or on-line algorithms. Aside from the algorithmic techniques, the goal of the course is also to familiarize the students with the mathematical tools necessary to prove performance guarantees.	Second year	First semester	6	ING-INF/05
10589621 - Advanced Machine Learning	General objectives: The course will present to students advanced and most recent concepts of machine learning and their application in computer vision via deep neural network (DNN) models. It will include theory and practical coding, as well as a final hands-on project. Towards the coding assignments and the final project, the students will work in teams and present their ideas and project outcome to the class. Specific objectives The first part of the course includes delving into state-of-the-art DNN models for classification and regression applied to detection (where the objects are in the image), pose estimation (whether people stand, sit or crunch) and re-identification (estimating a unique vector representation for each person). The course further discusses DNNs for multi-task objectives (joint detection, pose estimation, re-identification, segmentation, depth estimation etc). This first part would include DNNs which apply to video sequences, by leveraging memory (e.g. LSTMs) or attention (Transformers). The second part of the course delves into models, training techniques and data manipulation for generalization, domain adaptation and meta-learning. Further to transfer learning (how pre-trained models may be deployed for other tasks), it discusses multi-modal (with different sensor modalities such as depth or thermal cameras) and self-supervision (e.g. training the DNN model by solving jigsaw puzzles) to auto-annotate large amounts of data. Finally, it presents domain adaptation (e.g. apply daytime-detectors for night vision) and meta-learning, a most recent framework to learn how to learn a task, e.g. online or from little available data. Knowledge and understanding: At the end of the course students will be familiar with state-of-the-art DNN models for multiple tasks and multi-task objectives, as well as generalization and the effective use of labelled and unlabelled data for learning, self-supervision and meta-learning. Apply knowledge and understanding: At the end of the course students will have become familiar with the most recent advances in machine learning across a variety of tasks, their adaptation to novel domains and the continual self-learning of algorithms. They will be able to explain the algorithms and choose the most appropriate techniques for a given problem. They will be able to experiment with existing implementations and design and write programs for new solutions for a given task or problem in the two fields. Critical and judgment skills: Students will be able to analyse a problem or task and identify the most suitable methodologies and techniques to apply in terms of the effective resolution of the problem (accuracy) and its feasibility, including the efficiency, the required amount of data and annotation. Further to class discussions, critical and judgemental skills would be the result of assignments, a course project and a final project report. Communication skills: Students will acquire the ability to expose their knowledge in a clear and organized way, which will be verified through a final project presentation and its discussion. Students will be able to express their solutions rigorously and to explain the structure of the code they have written. Learning ability: The acquired knowledge will enable students to face the study of other problems in machine learning and computer vision. Learning ability would result from the chosen lecture topics, covering most broad areas in advanced machine learning, as well as from the final project, for which students would deep dive into a new topic, beyond the thought material.	Second year	First semester	6	INF/01
1056023 - Smart Environments	Goal of this course is to provide an overview of the large world of wireless and wired technologies that are will be used for the Smart Environments. These technologies will be able to provide infrastructures of networks and digital information used in the urban spaces and smart environments to build advanced applications. Recent advances in areas like pervasive computing, machine learning, wireless and sensor networking enable various smart environment applications in everyday life. The main goal of this course is to present and discuss recent advances in the area of the Internet of Things, in particular on technologies, architectures, algorithms and protocols for smart environments with emphasis on real smart environment applications. The course will present the communication and networking aspects as well as the processing of data to be used for the application design. The course will propose two cases studies in the field of smart environments: Vehicular Traffic monitoring for ITS applications and Network cartography. In both cases instruments, models and methodologies for the design of smart environments applications will be provided.	Second year	Second semester	6	ING-INF/03

**GRUPPO OPZIONALE C: The student must acquire 6 CFU from the exams below**
Course		Year	Semester	CFU	SSD
1047208 - STATISTICAL LEARNING	Devising new machine learning methods and statistical models is a fun and extremely fruitful “art”. But these powerful tools are not useful unless we understand when they work, and when they fail. The main goal of statistical learning theory is thus to study, in a statistical framework, the properties of learning algorithms mainly in the form of so-called error bounds. This course introduces the techniques that are used to obtain such results, combining methodology with theoretical foundations and computational aspects. It treats both the basic principles to design successful learning algorithms and the “science” of analyzing an algorithm’s statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own data analyses. Methods for a wide variety of applied problems will be explored and implemented on open-source software like R (www.r-project.org), Keras (https://keras.io/) and TensorFlow (https://www.tensorflow.org/). Knowledge and understanding On successful completion of this course, students will: know the main learning methodologies and paradigms with their strengths and weakness; be able to identify a proper learning model for a given problem; assess the empirical and theoretical performance of different learning models; know the main platforms, programming languages and solutions to develop effective implementations. Applying knowledge and understanding Besides the understanding of theoretical aspects, thanks to applied homeworks and a final project possibly linked to hackathons or other data analysis competitions, the students will constantly be challenged to use and evaluate modern learning techniques and algorithms. Making judgements On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical learning paradigms and techniques. Communication skills In preparing the report and oral presentation for the final project, students will learn how to effectively communicate original ideas, experimental results and the principles behind advanced data analytic techniques in written and oral form. They will also understand how to offer constructive critiques on the presentations of their peers. Learning skills In this course the students will develop the skills necessary for a successful understanding as well as development of new learning methodologies together with their effective implementation. The goal is of course to grow a active attitude towards continued learning throughout a professional career.	First year	Second semester	6	SECS-S/01
1047209 - QUANTITATIVE MODELS FOR ECONOMIC ANALYSIS AND MANAGEMENT	General Objectives of the course The general objectives of the course are: - Present a general framework for the development of quantitative models for economic analysis and management; - Provide the basic concepts and a guide to analyse the specialised literature; - Propose a unified framework on the main methodologies available to compare the productivity and efficiency of Decision Making Units (DMUs); - Introduce to the relevant roles played by the data for the development of effective quantitative models of socio-economic systems; - Make an introduction to the main softwares available to implement the quantitative models presented during the course; - Provide laboratory sessions to implement the quantitative models presented during the course in practice; - Present several applications in the field of economics and management, including public sector services as potential group project works, to be developed by the students according to their personal interest and background; - Interact with students through seminars, assisted laboratory, oral presentations and the realization of a project work on real data. Specific objectives of the course • KNOWLEDGE AND UNDERSTANDING: DEMONSTRATE THE KNOWLEDGE OF THE BASIC METHODS FOR THE DEVELOPMENT OF QUANTITATIVE MODELS FOR ECONOMIC ANALYSIS AND MANAGEMENT ; • ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: TO BE ABLE TO DEVELOP QUANTITATIVE ECONOMIC MODELS ON THE BASE OF THE KNOWLEDGE AND TECHNIQUES LEARNED DURING THE COURSE; • JUDGMENT AUTONOMY: TO BE ABLE TO DEVELOP A QUANTITATIVE ECONOMIC MODEL WITH CRITICAL SPIRIT, CHOOSING THE APPROPRIATE METHOD AND CORRECTLY IMPLEMENTING IT. • COMMUNICATION SKILLS: BEING ABLE TO COMMUNICATE THE RESULTS OF THE ANALYSIS AND ITS INFORMATION TO DIFFERENT TYPES OF INTERLOCUTORS; • LEARNING SKILLS: TO DEVELOP THE NECESSARY SKILLS TO APPLY AND DEVELOP AUTONOMOUSLY THE METHODS AND MODELS LEARNED DURING THE COURSE.	First year	Second semester	6	ING-IND/35
1041415 - OPTIMIZATION METHODS FOR MACHINE LEARNING	Knowledge and understanding The aim of the course is to introduce students to the application of optimization techniques to training problems arising in machine learning. Students are expected to gain insight into standard models in Machine Learning (Deep Networks and Support Vector Machines) and into more recent optimization algorithms for determining the parameters (training) of such models that best fit to the available data. Applying knowledge and understanding By the end of the course, students should be able to select the correct model for the problem at hand and either to use standard software specialized to the application and/or to develop their own optimization algorithm. modelli di apprendimento automatico applicati ai casi studio in apprendimento automatico. Making judgements Lectures, practical exercises and project sessions will provide students with the ability to assess the main strengths and weaknesses of the different machine learning models applied to case studies in machine learning. Communication By the end of the course, students are able to point out the main features of a machine learning problem and explain techniques for its solution both with a specialized and a non-specialized audience. These abilities are tested and evaluated in the projects developed in small groups thus encouraging team building and a proactive learning process coupling with collaborative learning. These abilities can also be checked in the final oral exam. Lifelong learning skills Students are expected to develop those learning skills necessary to undertake additional studies on the relevant topics with a high degree of autonomy. During the classes, students are encouraged to work on projects into small groups thus stimulating student activity and engagement. They are pushed to consult supplementary research publications and internet sites to exploit tricks and detailed choices needed to accomplish the tasks effectively. These capabilities are tested and evaluated in the development of the final reports of the projects where students have to discuss the main issues of the addressed problems and their choices to overcome the difficulties, based on the topics and material covered in class.	Second year	First semester	6	MAT/09
1056087 - STATISTICS FOR STOCHASTIC PROCESSES	The main objective of the course is to show the most popular techniques of non parametric inference, mainly from a Bayesian perspective. Covered topics include Dirichlet processes and its ramifications with special emphasis to applications of above methods in data science applications.	Second year	Second semester	6	SECS-S/01

**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	The aim of the course is to provide students with an overview of the functioning of intellectual property, competition and data protection law from both an economic and legal perspective. By the end of the course students are expected to have acquired a general understanding of the main policy issues involved, and should be able to identify and apply the relevant legal rules, both substantial and procedural, in situations that can be considered routinary to professionals and businesses operating in the data science industry.	First year	Second semester	6	IUS/04
1047212 - Economics of Network Industries	Knowledge and understanding The aim of the course is to introduce students to the new information economy and the economics of network industries. Students are expected to gain insight into how the specific features of technology and demand affect market structure, firms’ strategies and business models, as well as public policy in network industries. Applying knowledge and understanding By the end of the course, students should be able to use methods and models of microeconomics and industrial organization to understand and analyze the competitive dynamics in the new information economy, and specifically in network industries. Making judgements Lectures, practical exercises and problem-solving sessions will provide students with the ability to assess the main strengths and weaknesses of theoretical models when used to explain empirical evidence and case studies in the new information economy. Communication By the end of the course, students are able to point out the main features of the new information economy and network industries, and to discuss relevant information, ideas, problems and solutions both with a specialized and a non-specialized audience. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work. Lifelong learning skills Students are expected to develop those learning skills necessary to undertake additional studies on relevant topics in the field of the new information economy with a high degree of autonomy. During the course, students are encouraged to investigate further any topics of major interest, by consulting supplementary academic publications, specialized books, and internet sites. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work, where students may have to discuss and solve some new problems based on the topics and material covered in class.	First year	Second semester	6	SECS-P/06

**GRUPPO OPZIONALE D: The student must acquire 12 CFU from the exams below**
Course		Year	Semester	CFU	SSD
1056085 - Big Data for Official Statistics	What subset of Big Data can be used in the ambit of Official Statistics and what domains of Official Statistics can be enriched through the availability of new data sources. How new data sources can be used in Official Statistics, by taking into account challenges, needs and risks in this exercise. Definition of the role of Big Data in the context of Official Statistics. How to frame the measurement of social, demographic and economic phenomena through Big Data by considering challenges, needs and risks.	Second year	First semester	6	SECS-S/05
10589627 - Neural Networks for Data Science Applications	The course will introduce neural networks in the context of data science applications. After a brief overview on the basic theoretical concepts, the course will introduce recent techniques and algorithms (going under the broad name of “deep learning”), that have allowed to successfully apply neural networks to a wide range of problems, including computer vision and natural language processing. In particular, students will be introduced first to convolutional networks (for image analysis), and then to recurrent neural networks for processing large sequential data and tackle so-called ‘sequence to sequence’ problems (e.g., machine translation). Each topic will be supplemented by a practical laboratory where all concepts will be developed on realistic use cases through the use of the TensorFlow library, open sourced by Google two years ago. Students will acquire the ability to design and implement learning methods for a variety of real-world scenarios (e.g., image recognition and understanding), and will acquire the key concepts allowing them to master advanced sub-fields on their own (e.g., neural machine translation). Moreover, students will acquire proficiency in the use of Python for deep learning applications, most notably with the use of the Google’s TensorFlow framework.	Second year	First semester	6	ING-IND/31
10593052 - Bioinformatics and Network Medicine	General objectives. The general objectives of the course are: i) to provide students with a hands-on experience with basic biological concepts and common bioinformatics tools and databases; ii) to introduce students to the on-the-field application of networks in biology and medicine. Specific objectives. Students are expected to acquire basic biology knowledge and skills, to understand the role of networks in the study of physiological mechanisms and diseases; to understand how to use network medicine algorithms and procedures. Knowledge and understanding. The course will include theory and hands-on projects. Students will be trained in the basic theory and application of programs used for database searching, biological network inference and analysis. Apply knowledge and understanding. At the end of the course students will have become familiar with basic biological concepts and bioinformatics databases and tools. Furthermore, on successful completion of this course, students will understand the use of networks as a paradigm for disease expression and course. Critical and judgment skills. At the end of the course, students will be able to critically analyse the results of their analysis. Communication skills. The students will be required to produce reports describing the hands-on projects with specific sections for the description of the obtained results and their discussion. Learning ability. The projects will be developed in small groups encouraging team building. All the acquired abilities will be checked in a final oral exam during which a good division of teamwork will be rewarded.	Second year	First semester	6	ING-INF/06
10593053 - Digital Epidemiology and Precision Medicine	General objectives. Digital data sources and digital traces of human behaviour have the potential to provide local and timely information about disease and health dynamics at the population level. The general aim of the course is to introduce students to the analysis of epidemiological and omics data and to the use of computational approaches for medical/clinical purposes. Specific objectives. The course consists of two modules. The first module will deal with the opportunities and challenges of mining digital data sources for epidemiological and public health signals and will provide an overview of the state of the art of this emerging field. The second module will focus on “precision medicine”, an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. With the second module, the students are expected to acquire basic biology knowledge and skills and to become familiar with the analysis and integration of omics data. Knowledge and understanding. The course will include theory and hands-on lectures. Students will be trained in the basic theory for the identification of gene interactions and in the use of network science. Apply knowledge and understanding. At the end of the course students will have become familiar with basic biological concepts, with the analysis of omics and epidemiological data and with the use of networks for the investigation of infectious disease dynamics and disease etiology, diagnosis, and treatment. Critical and judgment skills. At the end of the course, students will be able to critically analyse the results of their analysis. Communication skills. The students will be required to produce reports describing hands-on projects with specific sections for the description of the obtained results and their discussion. Learning ability. The projects will be developed in small groups encouraging team building.	Second year	First semester	6	ING-INF/06
1047218 - EARTH OBSERVATION DATA ANALYSIS	The module aims at providing a general background on the remote sensing systems for Earth Observation from space‐borne platforms and on data processing techniques. It describes, using a system approach, the characteristics of the system to be specified to fulfil the final user requirements in different domains of application. Remote sensing basics and simple wave‐interaction models useful for data interpretation are reviewed together with technical principles of the main remote sensors. The course also provides an overview of the most important applications and bio‐geophysical parameters (of the atmosphere, the ocean and the land) which can be retrieved. The most important techniques for data processing and product generation, also by proposing practical exercises using the computer, are analysed together with an overview of the main Earth Observation satellite missions and the products they provide to the final user.	Second year	Second semester	6	ING-INF/02
1056129 - Data Driven Economics	A first objective of the course is to provide a basic toolbox for the analysis of agent and group interaction under uncertainty and asymmetric information, and its main consequences on markets enabled from large-scale digital platforms. A second objective of the course will be to provide the basic methods for the use of big data for estimating relevant economic indices. The active participation of students will be stimulated with game-theoretic examples, presentations, simple experiments, case-studies and projects involving the use of real-world data.	Second year	Second semester	6	SECS-P/02
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	This course has the target of providing the students with the modern techniques of measuring quantitatively advanced topics in economic statistics. In particular our focus will be on three main interrelated directions: 1) the analysis of production and efficiency, specifically in the private but also in the public sectors, 2) economic dynamics of sectorial systems founded on micro data, 3) growth, ICT and technology in the modern economy. This course uses statistical methods, both stochastic and deterministic, to analyze topics such as productivity, efficiency and growth at micro, sectorial, and for coherence at macro level. We first take into exam data from firms that will be useful for the mentioned three-levels study, then, as regards the efficiency analysis of productive units, such data will be employed in order to evaluate mergers and acquisitions of plants and firms and management of productive factors. Efficiency will be evaluated from the sides of costs, profits and revenues. As for the sectorial analysis, static and dynamic models will be considered to allow for forecasts and simulations in each sector for variables like production, labour, capital, raw materials, prices and capital gains. As a consequence, an aggregate analysis on the production, growth and prices will follow. We also deal with ICT and technical progress in the production process considering how and if the associated externalities are effective. We will use the following techniques for data analysis: accounting rules for the database, panel data econometrics, time series analysis for systems of equations, methods for differential equation systems. Topics on private and also public sectors will contribute to explain the relationship between economic structure and the actual crisis. Specifically, lectures also include the examinations of cases study concerning the efficiency and productivity analysis on the recent patterns of the banking sector in the international context.	Second year	Second semester	6	SECS-S/03
10589730 - Geomatics and Geoinformation	The vast majority (a percentage close to 80%) of the currently available data has a geographical connotation, is intrinsically linked to a position; they are therefore named geospatial data. Furthermore, the ever-increasing availability of sensors capable of acquiring geospatial data, allowing the acquisition of larger and larger amounts of data, raises several important issues related to the correct, efficient and effective use of these geospatial big data. This course therefore finds its motivation in the great availability and relevance of geospatial data (in particular big data), and it aims to provide the fundamentals on the main methodologies and techniques currently available for their acquisition, verification, analysis, storage and delivery. Special attention is given to data coming from global navigation satellite systems (GNSS), remote sensing and photogrammetry, volunteered geographic information (VGI) and crowdsourcing, both regarding their analysis and management with freely available software and open source software and their applications. As overall transversal competences, starting from the specific geomatic methods and techniques, students will be trained to develop skills to design both experiments encompassing geospatial big data acquisition, analysis and interpretation, and to use these data to solve interdisciplinary problems also with original approaches. Moreover, students will get the skills enabling the continuous and autonomous update of their methodological and technical competences during their professional life.	Second year	Second semester	6	ICAR/06

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 2019/2020
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.

Degree programme description

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.