First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
CIVIL ENGINEERING
Course unit
BIG DATA ANALYTICS DATA MINING FOR ENGINEERS
INP9087244, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
CIVIL ENGINEERING
IN0517, Degree course structure A.Y. 2017/18, A.Y. 2019/20
N0
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Degree course track SISTEMI E INFRASTRUTTURE DI TRASPORTO [004PD]
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination BIG DATA ANALYTICS DATA MINING FOR ENGINEERS
Department of reference Department of Civil, Environmental and Architectural Engineering
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit CANNOT be attended under the option Single Course unit attendance
Optional Course unit The Course unit is available ONLY for students enrolled in CIVIL ENGINEERING

Lecturers
Teacher in charge ROSA ARBORETTI GIANCRISTOFARO SECS-S/01

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP9087244 BIG DATA ANALYTICS DATA MINING FOR ENGINEERS ROSA ARBORETTI GIANCRISTOFARO IN0517

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Examination board not defined

Syllabus
Prerequisites: To have attended a basic course in Statistics.
Target skills and knowledge: Introduce the students to the main statistical techniques of Big Data analytics and Machine Learning within the contexts in which their use arises, making them understand both the theoretical foundations and their adequate application to concrete problems.
Examination methods: The exam consists of a written test on the computer related to a data analysis session with Machine Learning methods.
Assessment criteria: The students will be evaluated on the basis of the degree of knowledge of the theoretical principles and applications of the statistical methodologies presented within the class.
Course unit contents: This course focuses on the statistical analysis of data originating from engineering processes in order to allow the extraction of knowledge and to support decision-making actions. To this end, two classes of statistical models and methods are introduced: machine learning and data mining.
With machine learning we refer to algorithms and statistical models used to progressively improve their performance both in relation to a specific task assigned and in order to obtain forecasts without the need to be explicitly re-programmed. Data mining consists of a set of techniques and methodologies that have as their object the extraction of useful information from large volumes of digital data, both through automatic and semi-automatic methods.
In accordance with the modern paradigm of data-driven innovation, the course intends to introduce in a practical way the statistical methods and models necessary to face the upcoming challenges posed by complex systems and by the continuous technological change and business processes. This course also focuses on broad concepts, principles and techniques and establishes a baseline that can be applicable to any technological and industrial environment. The course is oriented to the practical analysis of engineering processes using KNIME software, as the main tool for statistical data analysis.
Planned learning activities and teaching methods: Lectures and exercises also through the use of slides and material previously provided to students, as well as exercises in the computer lab with the use of statistical software.
Additional notes about suggested reading: Slide and material provided by the teacher and textbooks.
Textbooks (and optional supplementary readings)
  • Gareth James • Daniela Witten • Trevor Hastie Robert Tibshirani, An Introduction to Statistical Learning with Applications in R. Heidelberg: Springer, 2009. http://faculty.marshall.usc.edu/gareth-james/ISL/ Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Minitab, Knime

Sustainable Development Goals (SDGs)
Industry, Innovation and Infrastructure