Master's Degree in Statistics and Information Management, with a Specialization in Data Analytics

Who is it for?

The Master's Degree Program in Statistics and Information Management, with a specialization in Data Analytics, is aimed at everyone whose concern for the analysis and treatment of information is critical to performing their roles, namely managers and technical staff involved in information production for strategic decision-making support in organizations. Marketing technical staff and managers, experts in market research and CRM, Information technologies managers, technical staff of Risk Management, and Information systems managers, are among the target groups of this program.

This program lasts 3 semesters: 2 correspond to the curricular component and 1 to the development of a thesis or work project and the realization of the Course Unit of Research Methodologies, in a total of 95 ECTS.

The curricular component (1st and 2nd semesters of the program) corresponds to 60 ECTS, of which 33,5 are in mandatory course units:

Mandatory course units

• Applied Multivariate Data Analysis
• Dissertation/ Work Project/ Internship Report
• Econometrics Methods
• Forecasting Methods
• Research Methods
• Sampling Theory and Methods
• Statistical Analysis
• Time Series Analysis

The remaining 26,5 ECTS corresponding to the Elective Course Units, will be chosen by the students among a wide range of available course units.

1st year - Fall Semester

• Applied Multivariate Data Analysis
• Forecasting Methods
• Statistical Analysis
• Time Series Analysis
• Applied Network Analysis
• Banking and Insurance Economics
• Business Intelligence I
• Business Process Management
• Change Management
• Computational Statistics I
• Data Governance
• Data Management and Storage
• Data Mining I
• Data Privacy, Security, and Ethics
• Database Management Systems
• Digital Transformation
• Fixed Income Securities
• Information Management Systems
• Information Systems Development
• Information Systems Governance
• Information Technologies Services Management
• Investments and Portfolio Management
• Life Insurance
• Monetary and Financial Statistics
• Non-Life Insurance
• Portfolio Management
• Predictive Analytics in Finance

1st year - Spring Semester

• Econometrics Methods
• Sampling Theory and Methods
• Statistical Treatment of Data
• Analysis of Discrete Data
• Analysis of Variance
• Architectures for Information Systems
• Banking and Insurance Regulation and Supervision
• Big Data Analytics
• Blockchain
• Blockchain & crypto assets
• Business Impact of Digital Projects
• Business Intelligence II
• Computational Statistics II
• Credit Risk Management
• Customer Relationship Management Systems
• Cybersecurity
• Data Mining II
• Data Visualization
• Data-driven decision making
• E-Business
• Emerging Technologies for Innovation
• Enterprise Cloud Mobility
• Financial Derivatives and Risk Management
• Financial Reporting
• Industry 4.0
• Information Project Management
• Innovation Management and Design Thinking
• Knowledge Management
• Leadership and People Management
• Longevity-Linked Securities & Derivatives
• Market and Liquidity Risk Management
• Operational and Business Continuity Risk
• Process Mining Powered By Nokia
• Smart and Sustainable Cities

2nd year - Fall Semester

• Dissertation/ Work Project/ Internship Report
• Research Methods

Goals

The goal of this program is to train technical staff and managers to:

• Develop techniques and methodologies for data collection
• Apply various methodologies, tools, and computational statistics to explore and analyze information to reduce levels of uncertainty associated with decision-making
• Communicate results, in written form or orally, adapting them to the level and interests of the audience.