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Infrastructures
Quantitative data
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Glossary
Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Language
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Context
Academic Year 2019/2020
School School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (475) Electrical Engineering
Track R2F - MOBILITY ENGINEERING
Programme Year 2

Course Details
ID Code 052712
Course Title DATA SCIENCE FOR MOBILITY
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester First Semester
Course Description Data science aims at developing processes to analyze and ultimately understand phenomena through data. It stands at the intersection of several broad areas (statistics, information science, and computer science) and it employs methods from machine learning, classification, clustering, data mining, data bases, visualization, and cloud computing. This course presents the structure of the typical data science pipeline and, for each of the process, it overviews the most relevant methods and algorithms used to analyze mobility data. The course follows a problem-driven approach in that the techniques are presented based on the type of data they can tackle may these be structured (tables), unstructured (plain text, xml files), graphs, or time-series. All the methods are discussed focusing on the fundamental theory underlying them and their peculiarity, next they are demonstrated using either Python or R notebooks. Topics discussed during the course include, but are not limited to, data and data representation, data preparation, regression, classification, clustering, evaluation of classification and clustering models, methods to analyze text, graphs, and time series.
Scientific-Disciplinary Sector (SSD)
Educational activities SSD Code SSD Description CFU
--
ING-INF/05
INFORMATION PROCESSING SYSTEMS
5.0

Schedule, add and removeAlphabetical groupProfessorLanguageCourse details
From (included)To (excluded)
--AZZZZCarman Mark James
manifesti v. 3.4.18 / 3.4.18
Area Servizi ICT
27/11/2021