Cultural and educational objectives
The Bachelor's degree program in Data Analytics primarily focuses on teaching methodologies and techniques of mathematical analysis, inferential statistics, exploratory data analysis, computer tools for database design and management, programming concepts, principles of research epistemology, econometric models, experimental and dynamic models. The acquired knowledge from various disciplinary areas allows graduates to have a broad and analytical understanding of tools for data management, processing, and presentation, with a strongly interdisciplinary and integrated approach. In addition to the correct use of technical tools, graduates are also trained in applying these tools in various fields such as economics, finance, social sciences, demography, biomedicine, environment, and energy.
During the degree program, students in Data Analytics are also trained in designing and implementing data-intensive case studies in collaboration with companies through internship periods.
What makes this program innovative is the combination of traditional courses with:
- Learning the use of major statistical software and data mining tools (such as SAS Miner and open-source tools like Weka, R, Python) through computer lab exercises, often with the assistance of expert trainers.
- Solving practical problems and case studies using data extracted from databases of companies (in the telecommunications or IT sectors) or public institutions (e.g., Istat or Inps).
The specific skills acquired by Data Analytics graduates include:
- Knowledge of statistical and computational techniques for processing and analyzing large, complex, and often unstructured data from various sources, including high-frequency data collection (e.g., from sensors).
- Understanding of statistical methodologies, data mining techniques, and optimization techniques for solving complex problems, with the ability to apply them in real-world contexts.
- Experience in analyzing relational big data from the internet and performing social network analysis.
- Knowledge of forecasting and monitoring techniques for evolving phenomena.
- Proficiency in using statistical software and specific programming languages like R and Python.
- Understanding of database management systems and distributed computing systems, including those based on cloud computing.
- Ability to communicate analysis results through presentations, reports, and the construction and use of explanatory visual representations.
