This website uses third party cookies to improve your experience. If you continue browsing or close this notice, you will accept their use.

LUISS Guido Carli

Logo LUISS

Management and Computer Science

Digitalization and subsequent development in the fields of artificial intelligence, robotics, applications and IT infrastructures represent an important change that permeates all other fields. Both large and small companies, public administration, government institutions and professionals are interested in producing and using data for a variety of purposes, from improving production processes to defining new models to generate value. Digital transformation thus challenges us to rethink traditional managerial logic and use evidence-based approaches.

It requires a new leading class to embrace the opportunities offered by new technologies and the market to create organizational change and to guarantee a control of business for new institutional assets.
The bachelor’s degree program in Management and Computer Science stands out from other courses offered by LUISS University in Economics, Business and Finance for the significant presence of technical and engineering topics, focused on computing and business analytics. These skills are also integrated with traditional foundation courses in Economics, Business Management, Law and Statistics.

Class: L-18 Scienze dell'economia e della gestione aziendale (Economics)

Presentation

The core of the program focuses on collecting, processing and analyzing data in order to generate new knowledge to improve processes for large and small companies as well as for startups. To develop these skills, students are given solid preparation on algorithms and coding starting in their first year, as well as database management and advanced analysis techniques. In their third year, students focus on data science applications such as cybersecurity, innovation and entrepreneurship.

The program is designed for high school graduates interested in engineering-based programs (electronics, information technology, management), statistics, mathematics and computer science. The course prepares students for roles as data scientists or to go on to master’s programs in Marketing, Operations and Finance.
The program is offered entirely in English and the university is currently developing exchange programs with several foreign universities. Through these partnerships, participants will be able to spend their final year abroad, creating a multicultural experience thanks also in part to the presence of foreign instructors and students.

The bachelor's degree program in Management and Computer Science combines economics, management and law courses – essential to critically analyze and understand the context companies and institutions operate in – with a strong focus on quantitative methods, in particular statistics, information technology and information processing. 

Employment and professional opportunities for graduates

Graduates are equipped to work for economic and financial institutions as well as businesses and public administration in positions commensurate with their three-year preparation.

Admission procedure

Get information about taking the admission test and on the admission procedure for international students.

LUISSMatix

LUISSMatix, organized by the new LUISS bachelor’s degree program in Management and Computer Science, is an online programming competition, consisting in 4 problems to be solved in 5 hours, which will be counted starting from when a participant connects to the server of the competition. It is therefore possible to choose the five hours that are more compatible with one’s duties!

The problems are similar to those of the regional selection for the Olimpiadi di Informatica.

For questions or further information please contact the organizers of the competition: Prof. Giuseppe F. Italiano e Prof. Luigi Laura.

Required Courses

I year – 2018-2019

I semester

Course SSD Credits
Fundamentals of Management SECS-P/08 8
Introduction to Computer Programming ING-INF/05 6
This course will introduce the foundations of computer programming and of data structures.  Students will practice computer programming using Python and will also gain familiarity with some other languages, such as HTML (HyperText Markup Language), which is used to create web pages, and JavaScript, which is used to create dynamic applications that run in a web browser. Students will learn how to perform numerical computations, process and transform text, draw graphics and animations, and build dynamic, interactive web sites.
Microeconomics SECS-P/01 8
MathematicsSECS-S/068
This course will introduce the foundations of computer programming and of data structures.  Students will practice computer programming using Python and will also gain familiarity with some other languages, such as HTML (HyperText Markup Language), which is used to create web pages, and JavaScript, which is used to create dynamic applications that run in a web browser. Students will learn how to perform numerical computations, process and transform text, draw graphics and animations, and build dynamic, interactive web sites.

II semester

Course SSD Credits
Algorithms ING-INF/05 8
This course will show how to employ algorithmic techniques to solve efficiently computational problems. Students will develop the proper skills to understand and define clear requirements to a problem, decompose it manageable pieces, assess alternative problem solving strategies, and to design an algorithm that efficiently solves the problem. Students will also acquire the ability to implement efficiently their algorithmic solutions using the programming skills obtained in “Introduction to Computer Programming”. The course will also provide a deep understanding of the impact of algorithms in today’s and tomorrow’s business and society.
Legal System in the Digital Age IUS/01 6
Performance Measurements SECS-P/07 8
Statistics SECS-S/01 8
This course will introduce probability theory and data-generating processes that lead to probability distributions. It will build probability distributions from empirical data, and density functions from histograms. Topics of integration will be introduced as motivated by probabilistic ideas and the transition from discrete data to continuous functions. The focus will not be on proofs nor on excessive hand computations; instead, ideas and concepts will be made concrete through visualizations and numerical computations. 

II year – 2019-2020

I semester

Course SSD Credits
Data Analysis for Business SECS-P/01 8
This course will show how to help organizations collect, analyze, store and interpret large-scale data in order to develop informed business strategies, by providing a framework to improve students' understanding of data analytics, and enhance their critical thinking and decision making. In particular, students will acquire skills to recognize business problems, gain an understanding of data collection techniques and principles of data analysis, learn how to take data from the technical domain, bridge the data gap between the technical domain and business analysts, analyze and present valuable findings and recommend action to business leaders. 
Databases & Big Data ING-INF/05 8
This course will provide students with an understanding and ability to effectively apply principles of data management. This is broader than traditional database management techniques as it includes systems supporting the volume and velocity which are typically attributed to Big Data. Students will apply knowledge of data query languages to relational databases and emerging large store NoSQL data systems. They will be able to access data from less structured systems through web services and lower level access to data available across the Internet, and data sourced from streams. Once data are collected, data management includes cleaning and initial structuring, using the skills outlined above, and then transforming data into structured forms required for exploration, visualization, and analysis. 
Quantitative Models for Data Science SECS-S/06 8
This course will provide a high level introduction to quantitative topics that arise in the full data science workflow, from the initial investigation and data acquisition to the communication of final results. After a gentle introduction to the elementary notions in estimation, prediction and inference, students will investigate different topics, including the exposure to different data types and sources, and the process of data curation and modeling for the purpose of transforming data into a format suitable for analysis. To enhance students’ computational and analytical abilities, the class will be taught through different case studies occurring in practical scenarios.
Social Network Analysis MAT/09 8
This course will introduce the main tools for the study of networks, which are ubiquitous in today’s society: our personal, social and professional life is organized around networks of friends and colleagues, which determine our information, influence our opinions, and shape our political attitudes. Economic and financial markets look like networks, and many other natural and social phenomena exhibit a marked networked structure. The course will explain in details how certain common principles permeate the functioning of these diverse networks and how the same issues related to robustness, fragility and interlinkages arise in several different types of networks. Students will apply social network analysis to understand socially meaningful outcomes in political action, consumer behavior and online interaction.

II semester

Course SSD Credits
Artificial Intelligence and Machine Learning ING-INF/05 8
The course will provide an in-depth understanding of the foundations, scope and approaches of artificial intelligence. In particular, it will focus on machine learning algorithms and to their application to problems in various disciplines. This will provide students with the basic ideas and intuition behind modern machine learning methods as well as an understanding of how, why, and when they work. The underlying theme will be statistical inference as it provides the foundation for most of the methods covered. Students in this course will not only gain a deep understanding of the foundational aspects of artificial intelligence and machine learning, but they will also acquire the practical skills necessary for their successful applications to new problems in science and industry. 
Business Law and ICT IUS/04 6
Digital Business and Workplace Technology SECS-P/10 8
MacroeconomicsSECS-P/018

III year – 2020-2021

I semester

Course SSD Credits
Business Cyberlaw IUS/04 6
Business and Marketing Analytics SECS-P/08 8
Finance and Financial Technologies SECS-P/09 8
Elective course-6

II semester

Course SSD Credits
2 Elective courses - 6+6

Additional credits

Activity Credits
Mandatory language 4
Laboratories 6
Other activities 4
Final project work 4
Total Credits 180

Possible electives*

Course
 Digital Innovation and Entrepreneurship
 Digital Ethics
 Digital Platforms and Business Ecosystems
 Data Analysis and the Bank System
Blockchain and Cryptocurrencies
This course will provide a high level introduction to modern cryptocurrencies. Students will learn about cryptographic building blocks and will be able to reason about their security, and how to use these primitives to construct simple cryptocurrencies. A particular emphasis will be given to the blockchain and to Bitcoin's consensus protocol, in order to highlight how its security comes from a combination of technical methods and clever incentive mechanisms. The course will also discuss the community and politics issues within cryptocurrencies and the way that cryptocurrencies interact with politics, law enforcement and regulation issues. 

*Elective courses are subject to change

The study plan is subject to change.