Specializing Master in Data Science for Management
Milan, Italy
DURATION
1 Years
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
29 Oct 2025
EARLIEST START DATE
Jan 2026
TUITION FEES
EUR 10,000 / per year
STUDY FORMAT
On-Campus
Introduction
The program in Data Science for Management offers comprehensive training in computational and statistical methods for management from a problem-solving perspective.
Students are taught by both academics and professionals employed in dynamic companies dealing with data analysis, prediction, and evidence-based decision-making. The program’s ambitious goal is to empower students with technical as well as soft skills which are increasingly required by companies around the world to cope effectively with the digital revolution and develop new business opportunities. In this way, graduates of the program will have access to a rapidly expanding and highly rewarding job market.
The Master has been designed around eight core courses (structured in lectures, labs, and seminars) that cover a variety of fields including database systems and programming, statistics, text and web mining, data analytics, and machine learning. Students acquire solid computational and statistical skills to tackle real business problems, exploring a variety of industry-standard tools (such as R, SAS, and Python); they will also be offered the opportunity to acquire the «Machine Learning with SAS Viya» certification.
The Master relies on an extensive network of dedicated partner companies and institutions that provide highly professional teaching, real-world case studies, and mentoring.
Admissions
Scholarships and Funding
- Number of scholarships available: 7
- Scholarship value: €2.000
Curriculum
Total ECTS: 60
Data Management and Warehousing
The course illustrates how to implement and technically maintain a data warehouse. The focus is on database data design, extraction, profiling and standardization along with data transformation. A detailed analysis of big data quality management is provided.
Software Development and Technologies for Business Intelligence
The course focuses on software development and Object Oriented Programming. Students will gain broad software development skills to be able to independently write procedures and functions to expand and automate data analysis studies and results.
Statistics and Probability
The aim of this course is to deepen the knowledge of inferential methods which are useful for empirical research in all areas of business economics, as well as banking, insurance, and finance. Together with the theoretical concepts, data sets derived from empirical studies will be analyzed. The open-source software environment for statistical computing and graphics R will be introduced and used throughout.
Data Visualization
This course covers the basics of data visualization and exploratory data analysis. We will be using several data visualization libraries in Python / R starting with simple datasets and then moving to economic and financial data. We’ll also be looking at how to treat errors and missing data to avoid the most common representation mistakes.
Management for Digital Enterprise
The course illustrates the business characteristics of a Digital Enterprise along with the impact of a Digital Enterprise on the Customer Experience. At the end of the course, students will be able to understand the importance of ensuring that Digital Enterprise initiatives have clear business objectives and identify the relationships of Digital Enterprise with specific enablers (Digital Marketing, Analytics, and Customer Relationship Management).
Text and Web Mining
This course focuses on extracting knowledge from the web by applying classification and clustering techniques on hypertext documents. Students are introduced to information retrieval and filtering methods. Practical applications for web information extraction and text categorization are presented.
Data Mining and Pattern Recognition
The purpose of this course is to provide step-by-step instructions for the entire data modeling process, with special emphasis on the business knowledge necessary to successfully use statistical models. Moreover, students will be able to select suitable approaches for pattern recognition and to compare alternative methods in order to implement the best decision process for the problem under study.
Business Intelligence and Data Analytics
This course illustrates the usage of data and analytics in modern business activities. The main focus is on Data preparation to create suitable multidimensional Database Marketing frameworks. Demand Segmentation and Scoring Models will be the practical applications.
Career Opportunities
Graduates of the Master in Data Science for Management are ideally suited to fill jobs in the Data Analytics area across a variety of industries, ranging from ICT to consulting, from banking and finance to insurance.
English Language Requirements
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Program Admission Requirements
Show your commitment and readiness for Grad school by taking the GRE - the most broadly accepted exam for graduate programs internationally.