The program prepares students to work effectively with heterogeneous, real-world data, training them to become experts in extracting useful insights for business.
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.
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
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.
Learning methods and key courses
- Top managerial education
- Combination of lectures and labs
- Field project Econometrics
- Linear algebra/Multivariate calculus
- Machine Learning
- Programming: Hadoop/Spark, Python, R, SQL
The Master in Big Data Management is a 12-month program of intensive training, designed to develop the unique skill set required for a successful career in the world of big data and business analytics. After the Induction Week, the Term 1 provides strong economic and analytical fundamentals, covering data management and statistics and providing an overview of the cutting-edge tools and techniques related to big data. During the Term 2 and 3, students experience the core business courses to build professional and personal competences. The Term 4 is dedicated to the Field Project during which students put their knowledge into practice.
Term 1 Preparatory Courses
- 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.
TERM 2 and 3 Core Courses
- 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.
Term 4 Field Project
Students complete the program with a field project that consists of individual assignments accomplished under the supervision of a company mentor and an academic tutor, oriented at solving a real company problem identified by LUISS Corporate Partners.
The project allows students to:
- Solve real world business problems
- Apply their relational and problem solving skills
- Develop their professional and personal network
Scientific Director and Committee
Giuseppe Ragusa, Assistant Professor of Economics, Department of Economics and Finance, LUISS Guido Carli University
Francesco Castanò, Chief Information Officer, Italian National Institute of Statistics – ISTAT
Riccardo Corsini, Vice President, Government & Public Affairs, WPP Italy
Simonetta Iarlori, Chief Operating Officer, Cassa Depositi e Prestiti
Ombretta Main, Head Directorate EU and International Affairs at Italian Competition Authority – AGCM
Gianni Riotta, Pirelli Chair Visiting Professor, Princeton University