Second year Courses

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:

• Text Mining and Search – 6 CFU (course taught in English)
• Attività a scelta dello studente – 12 CFU
• Stage – 6 CFU;
• Prova finale – 24 CFU

select 1 course among:

• Business Intelligence – 6 CFU
• Cyber Security for Data Science – 6 CFU
• Digital Signal and Image Management – 6 CFU
• Service Science – 6 CFU
• Social Media Analytics – 6 CFU
• Technological infrastructures for Data Science – 6 CFU

select 1 course among:

• 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
• Industry Lab – 6 CFU

select 1 course among:

• Economics for Data Science – 6 CFU
• High dimensional data analysis – 6 CFU
• Streaming Data Management and Time Series Analysis – 6 CFU

Courses Description:

Business Intelligence – 6 CFU

1. 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
2. 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
3. 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

1. 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
2. Vulnerabilities and attacks:
   • Errors in software, the “buffer overflow”
   • Flaws in the networks, sniffing and spoofing
   • Social engineering
   • Exfiltrating critical information
   • Denial of service
3. Defenses:
   • Maintenance of software
   • Filtering and monitoring on networks
   • Best practices
4. Cryptography:
   • Methods (symmetric key, public key)
   • Some tools (PGP, TLS)
   • Vulnerable applications of cryptography: bad implementations or usage
5. Security specifically in big data sets, frameworks and defenses
6. Case studies, incidents and some open source tools

Digital Signal and Image Processing – 6 CFU

1. Analog-to-digital conversion, processing and descriptive feature extraction in signals and images
2. Signals classification and recognition
3. Images/videos classification and recognition
4. Indexing and retrieval methods for signals/images/videos in large archives
5. Analysis of case studies

Service Science – 6 CFU

1. 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)
2. 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)
3. Lab: Knowledge-based services design
4. 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
5. 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

1. Tasks involved by text mining
   • Information Retrieval
   • Text summarization
   • Text classification and clustering
   • Extracting structured information from texts
   • others…
2. Plain and semi- structured text pre-processing and analysis;
3. Text representation
   • Indexing
   • Bag of words
   • Statistical Language Models
   • Graph-based representation
   • others…
4. Information Retrieval: text-based search engines and web search engines
   • Web Crawling
   • Link-based algorithms
   • Web meta-data
5. Information Retrieval models
   • Boolean Model
   • Vector Space Model
   • Probabilistic Models
6. 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

1. Show me the detailed programme 

Economics for DS – 6 CFU

1. Model fit and causal inference
   • Internal and external validity
   • Big data: new frontiers for economic analysis
2. Program evaluation and causal inference
   • Randomized and natural experiments
   • Differences-in-differences estimator
   • Matching estimator
   • Regression discontinuity
   • Instrumental variables
3. Comparison of alternative approaches
4. Forecasts and simulation
   • Structural models
   • Ex-ante policy evaluation
5. Big data and causal inference
   • Using big data to identify causal effect
   • Empirical applications using big data

High dimensional data analysis – 6 CFU

1. High-Dimensional Data
   • High-Dimensional Statistics: a paradigm shift
   • Algorithms and Inference
2. Multiple Testing
   • FWER and FDR control: basic procedures
   • FDP estimation
3. High-Dimensional Inference
   • High-Dimensional Variable Selection
   • Sample-Splitting Inference
   • Stability Selection
4. Graphical Models

Industry Lab – 6 CFU

Streaming Data Management and Time Series Analysis – 6 CFU

1. Nature of time series data
   • Representing time series: raw data, features extraction, modelling
   • Historical versus streaming data
   • Managing time series data: time series databases
2. Main time series mining tasks
   • Similarity and clustering
   • Classification, regression and forecasting
3. Statistical prediction
   • Optimal prediction
   • Optimal linear prediction
4. Discrete time stochastic processes
   • Autocovariance function
   • Stationarity, integration and ARIMA processes
   • Cross-covariance function
   • VAR processes
5. Unobserved component models
6. State-space form
   • Kalman filter
   • Maximum likelihood estimates
   • Smoothing
7. Non-parametric approaches based on machine Learning
   • Artificial Neural Networks
   • Support Vector Machines
8. Applications to real time series using R