LUISS Business School

Master in Big Data Management

LUISS Business School

Master universitario
In aula
Ufficiale / Riconosciuto
12 Mesi
  • Roma
14.000 €



The Master in Big Data Management program prepares students to work effectively with complex, real-world data and to create value from it.

Big Data Management focuses on improving the understanding of customer patterns to increase business and improve profitability.  Working in big data management requires a hybrid set of competencies: computer expertise with a good knowledge of advanced statistical techniques, a thorough understanding of the business world and excellent communication skills. Finding this set of competencies in a single person is rare. They need to be developed with the right mix of classroom and field learning.

The Master in Big Data Management curriculum is designed to prepare students to create analytical models and interpret them from a business-oriented perspective. It prepares young professionals to pursue a career as a data scientist or business analyst for companies including large industrials, consulting firms and marketing specialists.



  • Data Management for Big Data Introduction
    Overview of clustering computer frameworks: Hadoop & Spark.
  • Economics of Strategy
    Analytical toolkit and conceptual frameworks of economic science required for understanding and interpreting the economic world, making rational choices and defining successful business strategies.
  • Introduction to Big Data Infrastructure
    Basic concepts of data warehousing and the evolution of these concepts in an architecture for Big Data. Developers learn to write SQL queries against single and multiple tables, manipulate data in tables and create database objects. 
  • Introduction to Big Data Programming
    Practical introduction to data management and programming with R.
  • Introduction to Statistics for Data Scientists
    Basics of Statistics necessary to be a Data Scientist.
  • Accounting
    Introduction to the basic concepts and standards underlying financial accounting systems. Focus on the construction and interpretation of basic financial accounting statements.
  • Business Law
    Introduction to ethical and legal notions of privacy, anonymity, transparency and discrimination, in reference to the Community regulatory framework and its evolution in progress.
  • Financial Management
    Introduction to financial management, including historical behavior of financial time-series, time-value of money, portfolio optimization and measures of risk.
  • Organization & Human Resource Management
    Introduction to Industrial Organization, including pricing models, supply and demand models and network analysis.  While the course covers the theoretical part of such models, the focus is primarily empirical.
  • Strategy
    Skills and techniques in business strategy formulation and the strategic management of organizations.
  • Access Tools and Informational Discovery
    Understand the main concepts of Text Analysis and handle the techniques of Natural Language Processing (NLP). Particular attention on explaining the methods to extract relevant information from data, using Topic Detection and Modeling techniques.
  • Advanced Programming
    Advanced techniques of programming with R, including package development and reporting in markdown.
  • Advanced Visualizations
    Foundations for understanding current state of the art in data visualization. Enables use of advanced data exploration and visualization tools (R and Tableau) to create their front-end to business users.
  • Economic Forecasting
    Introduction to the practice of forecasting economic time series, including theoretical methodologies followed by an extensive application in R.
  • Econometrics
    Introduction to econometrics, including theoretical methodologies followed by an extensive application in R.
  • Machine Learning
    Introduction to machine learning, including both supervised and unsupervised learning algorithms.
  • Marketing Analytics
    Identify and understand digital marketing metrics to measure the success of both social media and traditional web marketing initiatives and campaigns.


> Top managerial education
> Combination of lectures and labs
> Field project Econometrics
> Linear algebra/Multivariate calculus
> Machine Learning
> Programming: Hadoop/Spark, Python, R, SQL
> Statistics



The Master is targeted for students with a BA or MS in Economics, Statistics, Engineering or other scientific disciplines. Fluent English and strong motivation are required.

Prova di ammissione

The LUISS Admission Test evaluates the applicant's skills, personal motivation and potential. It can be held on campus in Rome (Italy) or remotely via Skype.



Orario / turno

From Monday to Friday

Posti disponibili



via Nomentana 216 Rome



The Master provides students with 60 ECTS credits. It teaches them how to harness large amounts of data, design analytical models and interpret them to optimize business processes.

Among the competences provided:

  • Skills to collect, process and extract value from large and diverse data sets

  • Capacity to work with different computing tools in order to address complex problems

  • Imagination to understand, visualize and communicate findings to non-data scientists

  • Ability to create data-driven solutions that boost profits, reduce costs and improve efficiency


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14.000 €

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Master in Big Data Management

Master in Big Data Management