Insegnamenti secondo anno

In this page there is the list of courses of the second year:

For a general overview of the Master Degree Syllabus click here 

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

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 – 3 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