Data analysis and visualization

2021/2022

Programme:

Computer Science and Mathematics, Second Cycle

Year:

1 ali 2 year

Semester:

first or second

Kind:

optional

Group:

ECTS:

Language:

slovenian, english

Lecturers:

Res. Assist. Dr. Alen Orbanić, Prof. Dr. Alexander Keith Simpson

Hours per week – 1. or 2. semester:

Lectures

Seminar

Tutorial

Lab

There are no prerequisites.

similarity measures
methods of multivariate data analysis, a selection
analysis of symbolic data
analysis of large datasets
data visualization

Van Cutsem B.(Ed.): Classification and Dissimilarity Analysis (LNS 93). Springer, 1994.
Carroll, J.D., Green, P.E., Chaturvedi, A. Mathematical Tools for Applied Multivariate Analysis. Academic Press, 1997.
Berthold M., Hand D.J. (Eds.): Intelligent Data Analysis. Springer, 2007.
Billard L., Diday E.: Symbolic Data Analysis. Wiley, 2006.
Abello J., Pardalos P.M., Resende M.G. (Eds.): Handbook of Massive Data Sets (Massive Computing). Springer, 2002.
Rajaraman A., Leskovec J., Ullman J.D.: Mining Massive Datasets. CUP, 2013.
http://infolab.stanford.edu/~ullman/mmds.html
White T.: Hadoop. O'Reilley, 2011.
Wilkinson L.: The Grammar of Graphics (Statistics and Computing). Springer, 2005.
Ware C.: Information Visualization. Morgan Kaufmann, 2004.
Tufte E.R.: The Visual Display of Quantitative Information. Graphics Press, 2001.

The goal of the course is to introduce some modern methods for data analysis and visualization with their theoretical background, and to enable the students to use these methods by themselves or also to develope their own solutions.

Knowledge and understanding:
Understanding of basic concepts and methods of data analysis and visualization
Ability to select the right methods for data analysis and visualization and perform them using appropriate software tools.
Ability to interpret the obtained results.

Lectures, homeworks, home reading, project, consultations

Continuing (homework and seminar)
Final (project work)
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

Alen Orbanić:
ORBANIĆ, Alen. Tools for networks. V: ALHAJJ, Reda (ur.), ROKNE, Jon (ur.). Encyclopedia of social network analysis and mining. New York: Springer, cop. 2014, str. 2166-2175, ilustr. [COBISS-SI-ID 17145433]
ŠIROK, Brane, BIZJAN, Benjamin, ORBANIĆ, Alen, BAJCAR, Tom. Mineral wool melt fiberization on a spinner wheel. Transactions of the Institution of Chemical Engineers. Part A, Chemical engineering research and design, ISSN 0263-8762, 2014, vol. 92, issue 1, str. 80-90, ilustr. [COBISS-SI-ID 13057819]
BIZJAN, Benjamin, ORBANIĆ, Alen, ŠIROK, Brane, KOVAČ, Boštjan, BAJCAR, Tom, KAVKLER, Iztok. A computer-aided visualization method for flow analysis. Flow measurement and instrumentation, ISSN 0955-5986. [Print ed.], Aug. 2014, vol. 38, str. 1-8, ilustr. [COBISS-SI-ID 13484571]
Alex Simpson:
Alex Simpson:
EGGER, Jeff, MØGELBERG, Rasmus Ejlers, SIMPSON, Alex. The enriched effect calculus: syntax and semantics. Journal of logic and computation, ISSN 0955-792X, 2014, vol. 24, iss. 3, str. 615-654. [COBISS-SI-ID 17090137]
EGGER, Jeff, MØGELBERG, Rasmus Ejlers, SIMPSON, Alex. Linear-use CPS translations in the enriched effect calculus. Logical methods in computer science, ISSN 1860-5974, 2012, vol. 8, iss. 4, paper 2 (str. 1-27). [COBISS-SI-ID 17090905]