In this page there is the list of courses of the second year:
For a general overview of the Master Degree Syllabus click here
Mandatory Courses:
select 1 course among:
select 1 course among:
select 1 course among:
Courses Description:
Business Intelligence – 6 CFU
- Introduction to BI and Big Data Analytics
- Goal and rationale of BI systems
- The value of knowledge – digital economy and data driven decision making
- The Structure and subsequent evolution of BI and Big Data Analytics systems
- BI Architectures
- The Evolution of BI Architectures
- Decision Models on the basis of business functions
- Definition, selection and metrics for computing directional indicators (KPI – CSF)
- Industrial case studies
- Knowledge Discovery in Databases – KDD
- Phases, methodologies and the value for business purposes (Data as value)
- Models for data quality evaluation – structured data vs (unstructured) Big data
- Models for data management and analytics – relational vs schema free (i.e., graph db)
- Models and techniques for data analysis – how to use data for fact-based decision making
- Visualization models for decision making – selecting the proper model for each stakeholder – data story telling and indicators
- Industrial case studies, practical laboratory
Cyber Security for Data Science – 6 CFU
- Introduction to cybersecurity:
- Founding principle, specific problems arising in computer science
- Actors involved: software developers, attackers, system admin, analysts
- Goals: confidentiality, integrity, availability
- Some real incidents
- Vulnerabilities and attacks:
- Errors in software, the “buffer overflow”
- Flaws in the networks, sniffing and spoofing
- Social engineering
- Exfiltrating critical information
- Denial of service
- Defenses:
- Maintenance of software
- Filtering and monitoring on networks
- Best practices
- Cryptography:
- Methods (symmetric key, public key)
- Some tools (PGP, TLS)
- Vulnerable applications of cryptography: bad implementations or usage
- Security specifically in big data sets, frameworks and defenses
- Case studies, incidents and some open source tools
Digital Signal and Image Processing – 6 CFU
- Analog-to-digital conversion, processing and descriptive feature extraction in signals and images
- Signals classification and recognition
- Images/videos classification and recognition
- Indexing and retrieval methods for signals/images/videos in large archives
- Analysis of case studies
Service Science – 6 CFU
- Introduction to Service Science
- The characteristics of services and their delivery process
- Porter value chain of service sector
- The role of information and knowledge to innovation of services
- Service systems design (from engineering model to interpretative model)
- Business strategies of service companies
- Evolution of business processes
- The role of value co-production (network companies)
- Knowledge-based services (crowdsourcing and open innovation processes)
- Social Media Analytics supporting the innovation of services (Tools and metrics of evaluation of Social Media based strategies)
- Lab: Knowledge-based services design
- Open data and public services
- From e-government to open government
- Models and techniques of open data publication
- Design models of open data based services
- Case studies
- Big Data and services
- Case histories of public services based on Big Data
Social Media Analytics – 6 CFU
Technological infrastructures for Data Science – 6 CFU
Text Mining and Search – 6 CFU
- Tasks involved by text mining
- Information Retrieval
- Text summarization
- Text classification and clustering
- Extracting structured information from texts
- others…
- Plain and semi- structured text pre-processing and analysis;
- Text representation
- Indexing
- Bag of words
- Statistical Language Models
- Graph-based representation
- others…
- Information Retrieval: text-based search engines and web search engines
- Web Crawling
- Link-based algorithms
- Web meta-data
- Information Retrieval models
- Boolean Model
- Vector Space Model
- Probabilistic Models
- Tools for text mining and search
Data Science Lab in Biosciences – 6 CFU
Data Science Lab in Business and Marketing – 6 CFU
Data Science Lab in Environment and Physics – 6 CFU
Data Science Lab in Medicine – 6 CFU
Data Science Lab in Public Policies and Services – 6 CFU
- Show me the detailed programme
Economics for DS – 6 CFU
- Model fit and causal inference
- Internal and external validity
- Big data: new frontiers for economic analysis
- Program evaluation and causal inference
- Randomized and natural experiments
- Differences-in-differences estimator
- Matching estimator
- Regression discontinuity
- Instrumental variables
- Comparison of alternative approaches
- Forecasts and simulation
- Structural models
- Ex-ante policy evaluation
- Big data and causal inference
- Using big data to identify causal effect
- Empirical applications using big data
High dimensional data analysis – 6 CFU
- High-Dimensional Data
- High-Dimensional Statistics: a paradigm shift
- Algorithms and Inference
- Multiple Testing
- FWER and FDR control: basic procedures
- FDP estimation
- High-Dimensional Inference
- High-Dimensional Variable Selection
- Sample-Splitting Inference
- Stability Selection
- Graphical Models
Industry Lab – 6 CFU
Streaming Data Management and Time Series Analysis – 6 CFU
- Nature of time series data
- Representing time series: raw data, features extraction, modelling
- Historical versus streaming data
- Managing time series data: time series databases
- Main time series mining tasks
- Similarity and clustering
- Classification, regression and forecasting
- Statistical prediction
- Optimal prediction
- Optimal linear prediction
- Discrete time stochastic processes
- Autocovariance function
- Stationarity, integration and ARIMA processes
- Cross-covariance function
- VAR processes
- Unobserved component models
- State-space form
- Kalman filter
- Maximum likelihood estimates
- Smoothing
- Non-parametric approaches based on machine Learning
- Artificial Neural Networks
- Support Vector Machines
- Applications to real time series using R