First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICS FOR ECONOMICS AND BUSINESS
Course unit
STATISTICAL METHODS FOR BIG DATA
SCP4063754, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course First cycle degree in
STATISTICS FOR ECONOMICS AND BUSINESS
SC2095, Degree course structure A.Y. 2014/15, A.Y. 2018/19
N0
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL METHODS FOR BIG DATA
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-lauree-triennali
Department of reference Department of Statistical Sciences
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge BRUNO SCARPA SECS-S/01

Mutuating
Course unit code Course unit name Teacher in charge Degree course code
SCP4063754 STATISTICAL METHODS FOR BIG DATA BRUNO SCARPA SC2094

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 9.0

Course unit organization
Period Second semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 4.0 30 70.0 No turn
Lecture 5.0 34 91.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
3 Commissione a.a.2018/19 01/10/2018 30/09/2019 SCARPA BRUNO (Presidente)
CANALE ANTONIO (Membro Effettivo)
FINOS LIVIO (Membro Effettivo)
MENARDI GIOVANNA (Membro Effettivo)

Syllabus
Prerequisites: Linear Algebra, Computer science, Statistics, Statistical models, Multivariate data analysis
Target skills and knowledge: The growth of web and the development of new technological tools to collect and save huge quantities of data require new statistical tools to consider large size, dimension and complexity of big data.
New types of data, such as functions, graphs, networks are now available and need for new methods for statistical analysis.
The course presents some of the techniques and methods used in this context
Examination methods: Practical and oral exams
Assessment criteria: Elements of evaluation:
- choice of the dataset as example of big data (size, dimension, complexity,...)
- quality of the statistical analysis
- clarity and organicity of the report
- quality of the presentation and of the discussion of the report
- correctness and quality of the oral exam
Course unit contents: Visualization of big data
Dimensionality reduction (ICA, principal curves and surfaces, t-sne)
Methods for large p and small n: penalization, ridge regression and lasso. Efficient algorithms
Introduction to analysis of functional data
Analysis of network data
Text mining and Sentiment analysis (iSA)
Association rules
Computational Statistics: parallel, recursive and dynamic algorithms for statistics. Recursive estimate for linear models and dynamic linear models (Kalman filter)
Planned learning activities and teaching methods: Class lessons. Laboratory sessions
Additional notes about suggested reading: Materials will be available on the elearning platform of the course
Textbooks (and optional supplementary readings)
  • Scarpa, B., Materiale didattico via web per l'insegnamento di Big Data. --: --, --.
  • Azzalini, Adelchi; Scarpa, Bruno, Data analysis and data miningan introduction. New York: Oxford University Press, 2012. Cerca nel catalogo
  • Ceron, Andrea; Curini, Luigi, Social media e sentiment analysisl'evoluzione dei fenomeni sociali attraverso la rete. Milano: Springer, 2014. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Questioning
  • Action learning
  • Story telling
  • Problem solving
  • Concept maps
  • Peer feedback
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Latex
  • R, Python

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure