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Master Data Anlytics
Fundamentals

“MDA – Fundamentals” is a program meticulously crafted to provide students with a robust foundation and advanced expertise in the dynamic field of data analytics.

The curriculum is designed to impart both theoretical understanding and practical skills, preparing students to tackle complex data challenges. It covers core concepts such as statistical analysis, machine learning algorithms, and data manipulation techniques, ensuring graduates are well-equipped for a career in data-driven industries. All lessons are, and always have been, available online

Professional Development

Building upon existing academic knowledge, the MDA – Foundamentals provides a comprehensive training in data analytics techniques.

Industry Relevance

Keeping pace with the latest trends in machine learning and deep learning, the program ensures graduates are well-prepared for the demands of modern data-driven industries.

Practical Skills

Developing hands-on skills in data management, big data analysis, text mining, and domain-specific data analysis.

Value Extraction

Learning to extract valuable insights and information from data to inform strategic decisions.

Program Structure

The one-year MDA – Foundamentals program, starting in January 2026 and concluding in January 2027, offers over 60 ECTS credits. The program includes a mix of lectures, exercises, seminars, and a final project, totaling more than 300 hours of instruction.

Flexible Learning

All lectures are delivered online, allowing students to attend live streams or recorded sessions.

Multilingual Instruction

Lectures are conducted in both Italian and English.

By the end of the program, students will have a strong foundation in data analytics, enabling them to excel in a variety of data-driven roles.

A modular approach

We employ a modular approach, dividing our courses into five distinct areas. The first group covers fundamental coding skills, while the remaining four groups delve into specialized topics:

Statistics

These lessons offer a comprehensive introduction to statistical modeling and analysis, showing how probability and statistics are essential for managing data and uncertainties in data. The course covers core concepts like probability, regression, statistical inference, Bayesian methods, and the specialized field of Extreme Value Theory (EVT).

Advanced Coding

Introduce fundamental instruments and technologies that involve different fields of study: domain expertise, programming skills (based on Python and his main libraries) and knowledge of statistics to extract meaningful insights from data. The key foundational concepts are defining the problem, translating it into a data-related problem, performing data Collection, Data Cleaning and Preprocessing (using a SQL-database like tool). Use statistical methods and visualization tools to explore patterns, correlations, and trends in data.

Artificial Intelligence

Learn to analyze data using modern techniques from Machine Learning and Deep Learning to reach better decisions. Then move beyond the basics by learning how to fine-tune, scale, and interpret your models. The main tools covered in the lectures include Jupyter Notebook, Anaconda, MATLAB, and TensorFlow.

CyberSecurity

The lessons provide an introduction to Cybersecurity, the practice of protecting computer systems, networks, and data from unauthorized access, use, disclosure, disruption, modification, or destruction.

Social Data and Natural Language Processing

This topic provides an introduction to Text Analytics and Natural Language Processing (NLP), exploring fundamental techniques, tools, and applications. A key focus will be on Large Language Models (LLMs), sophisticated AI systems trained on massive amounts of text and code.

Study Plan

Students can arrange a personal Study Plan according to the following rules:

To obtain your MDA – Foundamentals diploma, a minimum of 45 ECTS obtained by passing courses assignments are required.

The professor sets course assignments, which range from multiple-choice questions and coding tasks to comprehensive projects. Some of these assignments can be undertaken collaboratively in small groups.

Final project (thesis and stage) is mandatory for every student, for a total of 15 ECTS

Upon successful completion of the whole program, students are granted 60 ECTS credits.

Students will acquire in-depth understanding of the field through a rigorous and challenging program covering different areas: Machine Learning, analysis of Social Data and Business Intelligence, the digitalization of Public Services and Data Security. This will prepare students for successful professional careers.

Graduates from any Course Degree from Roma Tre University will benefit of a 60% reduction in tuition fees. Students can also choose to enroll to single specializations or to single courses, and will receive a certificate of attendance after successful completion of their program. The Scientific Committee can advise the prospective students about personalized programs.

Call for Application

Application closes on January, 19, 2026.
Late enrollment is possible until Mach 30 with a 100 euros fee.

Apply now