Digital content processing

Course objectives

The course aims to provide students with knowledge relating both the structure of the computers and the operating systems. It continues by providing theoretical and methodological knowledge for the collection, representation and analysis of heterogeneous data. The course concludes by introducing technological and innovative aspects represented by current computer networks.

Channel 1
MATTEO CINELLI Lecturers' profile

Program - Frequency - Exams

Course program
Introduction to Data Science Introduction to a data analysis language (R) Overview of libraries for data manipulation and management Overview of libraries for data visualization Exploratory Data Analysis Main types of data visualizations Examples of univariate, bivariate, and multivariate analysis Introduction to time series analysis Introduction to Complex Networks Basic concepts and structure of complex networks Community detection algorithms Data Collection Techniques
Prerequisites
Basic notions of mathematics and statistics
Books
- Wickham, H., & Grolemund, G. (2017). R for data science (Vol. 2). Sebastopol, CA: O'Reilly. - Newman, M. (2018). Networks. Oxford university press. - Network Science, A.L. Barabàsi http://networksciencebook.com
Frequency
Classes in person
Exam mode
Oral exam
Bibliography
- Wickham, H., & Grolemund, G. (2017). R for data science (Vol. 2). Sebastopol, CA: O'Reilly. - Newman, M. (2018). Networks. Oxford university press. - Network Science, A.L. Barabàsi http://networksciencebook.com
Lesson mode
In person lessons
  • Lesson code1049427
  • Academic year2025/2026
  • CourseEconomics and communication for management and innovation
  • CurriculumSingle curriculum
  • Year2nd year
  • Semester1st semester
  • SSDINF/01
  • CFU9
  • Subject areaAttività formative affini o integrative