Structure of the Program
The Master in Big Data Management is a 12-months 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.
Each term is described in detail below:
- 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.
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.
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.
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.
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