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
select 1 course among:
select 1 course among:
Data Management and Data Visualization – 12 CFU (course taught in English)
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 (import.io, 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)
- 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
- 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
- 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
- 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)
- 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
- 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:
- The television
- Political communication
- The Law of Internet
- journalism and libel in the web
- Requisition of websites
- Duty of the provider
- 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)
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
- 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
- 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.