Course Syllabus

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At theend of the module students will be able define and realize a complete data pipeline from data acquisition to data storage (relational or not) according to their application needs

Students will be able to evalute for each step the approrpiate tool to use

Introduction to data management in big data context

data life cycle
data acquisition techniques
data modelling and storage
data preparation, cleansing, quality and explorative data analysis
advance data management concepts

  1. Data life cycle
  2. data acquisition
  3. web scraping
  4. rest api
  5. real time data acquisition
  6. use of LLM
  7. data storage and modelling
  8. Introduction to NoSQL models
  9. key value and columnar models
  10. Document based system
  11. Graph db
    6.data preparation, cleansing, quality and explorative data analysis
  12. Data integration
  13. Data quality
  14. Advanced data management concepts
  15. data warehouse
  16. data lake

knowledge of relational model

Lectures and exercises in the classroom and on virtual lab

Lectures with the support of slideware, discussion of practical cases through the forum, discussion of practical home-work projects.

Some self-assessment tests, not considered for the final evaluation will be provided

G. Harrison Next Generation Databases, Apress, 2015

A. Rezzani Big data analytics Apogeo 2017

Yau, N. (2011). Visualize this: the FlowingData guide to design, visualization, and statistics. John Wiley & Sons.

Ware, C. (2012). Information visualization: perception for design. Elsevier.

Scientific articles and class pack provided by the lecturers.

first semester

The exam is divided into two parts

Data Management (50% of the final evaluation): Written exam and a project related to the topic of the module

Data visualization(50% of the final evaluation): test and a project related to the topic of the module

Please send an e-mail to teachers to arrange an appointment

ISTRUZIONE DI QUALITÁ | IMPRESE, INNOVAZIONE E INFRASTRUTTURE
Export

At theend of the module students will be able define and realize a complete data pipeline from data acquisition to data storage (relational or not) according to their application needs

Students will be able to evalute for each step the approrpiate tool to use

Introduction to data management in big data context

data life cycle
data acquisition techniques
data modelling and storage
data preparation, cleansing, quality and explorative data analysis
advance data management concepts

  1. Data life cycle
  2. data acquisition
  3. web scraping
  4. rest api
  5. real time data acquisition
  6. use of LLM
  7. data storage and modelling
  8. Introduction to NoSQL models
  9. key value and columnar models
  10. Document based system
  11. Graph db
    6.data preparation, cleansing, quality and explorative data analysis
  12. Data integration
  13. Data quality
  14. Advanced data management concepts
  15. data warehouse
  16. data lake

knowledge of relational model

Lectures and exercises in the classroom and on virtual lab

Lectures with the support of slideware, discussion of practical cases through the forum, discussion of practical home-work projects.

Someelf-assessment tests, not considered for the final evaluation will be provided

G. Harrison Next Generation Databases, Apress, 2015

A. Rezzani Big data analytics Apogeo 2017

Yau, N. (2011). Visualize this: the FlowingData guide to design, visualization, and statistics. John Wiley & Sons.

Ware, C. (2012). Information visualization: perception for design. Elsevier.

Scientific articles and class pack provided by the lecturers.

first semester

The exam is divided into two parts

Data Management (50% of the final evaluation): Written exam and a project related to the topic of the module

Data visualization(50% of the final evaluation): test and a project related to the topic of the module

Please send an e-mail to teachers to arrange an appointment

QUALITY EDUCATION | INDUSTRY, INNOVATION AND INFRASTRUCTURE
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Key information

Field of research
INF/01
ECTS
6
Term
First semester
Activity type
Mandatory
Course Length (Hours)
46
Degree Course Type
2-year Master Degreee
Language
English

Staff

    Teacher

  • RA
    Roberto Avogadro
  • AM
    Andrea Maurino

Enrolment methods

Manual enrolments
Self enrolment (Student)

Sustainable Development Goals

QUALITY EDUCATION - Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
INDUSTRY, INNOVATION AND INFRASTRUCTURE - Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation