Master in Data Analytics for Economics and Management

The programme is divided into two tracks: Data Analytics for Economics and Business Analytics.

Both tracks share a strong methodological core in computer science, statistics, mathematics, and econometrics. The specific goals of the tracks are:

• Provide a strong foundation in theoretical and applied statistics and in machine learning, which are essential tools for developing effective models and methods for data analysis and prediction.
• Develop computer science skills that are crucial for building algorithms and programming tools suitable for the implementation and application of data analysis methods.
• Develop a profile that combines solid quantitative skills with domain knowledge in economics and business to support management in making data-driven strategic decisions.
• Develop problem-solving skills, teamwork skills, the ability to convey results effectively, and an understanding of ethical and legislative issues related to the use of big data.

ECTS credits: 120

1st Year

Programming and Visualization for Data Science

• M1 - Introduction to Programming for Data Science
• M2 - Data Visualization and Exploration

Econometrics for Data Science

• M1 - Time Series Analysis and Forecasting
• M2 - Management of Economic and Business Data

Statistical Methods

• M1 - Statistical Methods for Business Analysis
• M2 - Advanced Statistics

Machine Learning

Study Path - Data Analytics for Economics (Choose 2)

• Financial Mathematics
• Financial Econometrics
• Economic Policy
• Methods for Public Policies Evaluation

Study Path - Business Analytics (Choose 2)

• Network Thinking and Agent-based Modeling
• Marketing B2B and Sales Management
• Experiential Tourism Marketing

2nd Year

Optimization

• Optimisation Methods for Decision Making
• Optimisation Methods for Economics and Business

Data Science Applications

• Data Science Applications for Resource Optimisation, Risk Evaluation, and Sustainability

Big Data

• Big Data Methods for Economics and Business
• Statistical Methods for High-dimensional Data
• Natural Language Processing and Web Analytics

Cybersecurity and Digital Privacy

Study Path - Data Analytics for Economics (Choose 1)

• Financial Engineering and Quantitative Investment Strategies
• Applied Resource and Energy Economics *
• Sustainability Economics *

Study Path - Business Analytics (Choose 1)

• Digital Marketing Methods and Consumer Experience
• Performance Analytics for Business *
• Introduction to Blockchain (12 CP)*

Free Choice Courses

Internship (12 CP)

Thesis

* These two courses are offered in alternate years

Exchange programmes and Internships

We currently offer exchange programmes with partner universities around the world and cooperate with an extensive network of companies for internships in Italy or abroad. Attendance in Bozen-Bolzano can be combined with an exchange with a foreign university, in Europe or elsewhere.

During the two years of the programme, you will also complete an internship of at least 150 hours in the local administrations, in the Chambers of Commerce, in public companies (e.g. health, transport, school), in non-profit organizations or European or international organizations, depending on the availability of places and your preferences.

This study program can open a variety of exciting and well-paid career paths. Graduates will have acquired the skills needed to work as professional business analysts, data scientists, managers, or consultants in organizations that operate in a data-driven society.

Additionally, they will acquire the background necessary for enrolling in Ph.D. programs in fields closely connected to quantitative economics, business analytics, and data science. Skilled data analysts are some of the most sought-after professionals in the world now. Because of the strong demand and very limited supply of graduates, data analysts often receive high salaries and excellent job prospects, even at the entry level.

Data analyst jobs are available in a wide range of private industries and public organizations since any company using data also requires staff with a high level of analytic competency.