First year Courses

In this page you will find  the list of courses of the first year.

For a general overview of the Master Degree Syllabus click here 

Mandatory courses:

select 1 course among:

select 1 course among:

Course Descriptions:

Data Management and Data Visualization – 12 CFU (course taught in English)

Course Syllabus 

Data Managment module

  • Data life cycle
  • NoSQL models
  • Data distribution
  • Data quality
  • Geographical information systems
  • Architectures for big data analysis

Data Visualization module

  • Introduction to Visualization.
  • Human Perception and Information Processing
  • Data types
  • Graphical perception (the ability of viewers to interpret visual
  • (graphical) encodings of information and thereby decode information in graphs
  • Color for information display
  • Color management systems
  • Picture visualization and fruition
  • Data Transformation into sources of knowledge through visual representation.
  • Requirements and heuristics for high-quality visualizations.
  • Charts and standard views: relevance and appropriateness.
  • Advanced and innovative tools for data visualization and advanced quantitative analysis.
  • The evaluation of the quality of visualizations and infographics.
  • Workshops in which students will acquired practical skills to:
    • extract unstructured data from web (, kimono, etc.)
    • manage and manipulate data in tabular format (google spreadsheet, excel, etc.)
    • explore and present static data (RAWGraphs, Gephi, illustrator, etc.)
    • explore and build interactive data visualizations (Tableau Public, Carto)
    • design a “data-driven” narrative in a data journalism context.

Data semantics – 6 CFU (course taught in English)

Course Syllabus

  • Data semantics: from conceptual modeling to conceptual data management
  • Conceptual data management: Knowledge Graphs (KGs) and beyond
  • Semantic data modeling
  • Lab I: Data lifting by mapping tables to KGs
  • Semantic information integration
  • Semantic data enrichment
  • Lab I: Data integration and enrichment

Data science lab – 6 CFU

Course Syllabus

  • The R language
  • R markdown
  • R packages for statistical/machine learning
  • Sas Enterprise Miner for data mining
  • Guided applications to real data and problems
  • Workgroups on real data science problems and/or Kaggle competitions
  • Presentation of case studies by guest data scientists

Foundations of computer science – 6 CFU

Course Syllabus

  • Organizing raw datasets
  • Introduction to data bases.
  • Introduction to programming in Python.
  • Explorative programming. Managing tabular data.
  • Introduction to testing and debugging.

Foundations of probability and statistics – 6 CFU

Course Syllabus

  • Introduction to the data management with SAS
  • Descriptive analysis
  • Calculus and random variables
  • Inference (Estimators, Confidence Intervals, Significance test)
  • Linear models (Regression, Anova)
  • Introduction to Generalized Linear models (Logistic and Poisson regression)

Information systems – 6 CFU (course taught in English)

Course Syllabus

  • Introduction to Information Systems
  • A language for process modeling Business Process Modeling Notation
  • Efficiency and effectiveness of Information Systems
  • A methodology for the life cycle of ISs
  • The Boat framework.
  • Case studies.

Juridical and social issues in information society – 6 CFU

Course Syllabus

  • Introduction to public law
  • The freedom of speech and debate through the press (art. 21 Cost.):
  • The interpersonal communication (art. 15 Cost.)
  • The journalist job:
  • Privacy
  • The television
  • Political communication
  • The Law of Internet
  • journalism and libel in the web
  • Requisition of websites
  • Duty of the provider
  • Copyright
  • Digital Economy: Why Data is the new oil
  • The impact of digital innovation on employment and wealth distribution
  • The impact of the Internet on Society. Privacy and security in the data economy.
  • Government, citizens and Public Administration towards the digital age
  • Ethical issues in Information Society
  • The challenging role of the Data Scientist. New jobs and the Industries of the future

Machine Learning & Decision Models – 12 CFU (course taught in English)

Course Syllabus

Machine Learning Module

  • Introduction to Data Mining
  • Supervised and Unsupervised Classification
  • Association Analysis

Decision Models Module

  • Decision Analysis
  • From data to decision
  • Information value

Statistical modeling – 6 CFU

Course Syllabus

  • Statistical modeling for data analysis
  • Specification, estimation and verification of the interpretative advanced linear models compared to the classical linear model.
  • Generalized linear models that do not meet the assumptions of the classical linear model: models with esteroschedastici and related errors, non-linear models, the treatment of outliers
  • Multivariate linear models of classic and not
  • Multilevel models
  • Analyses of empirical cases with R

Web marketing and communication management – 6 CFU

Course Syllabus

  • Focus on digital marketing in the environment of multichannel marketing, evolution of the marketing services. Players, business models, services offered.
  • Sales Marketing and web marketing. Communication and marketing models: what’s new. In the digital era the target group: which processes are useful to achieve efficiency. Decision Support Systems.
  • Marketing Mix and traditional marketing. Econometrics and DSS. Customer Experience Leadership.
  • Customer Experience Strategy. Custome Journey. From CRM to Event Based Marketing. Event based Marketing: Tools. IT Architectures and business flows.