Data analysis and visualization

2023/2024
Programme:
Computer Science and Mathematics, Second Cycle
Year:
1 ali 2 year
Semester:
first or second
Kind:
optional
Group:
B
ECTS:
6
Language:
slovenian, english
Hours per week – 1. or 2. semester:
Lectures
2
Seminar
1
Tutorial
2
Lab
0
Prerequisites

There are no prerequisites.

Content (Syllabus outline)

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

Readings

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.

Objectives and competences

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.

Intended learning outcomes

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.

Learning and teaching methods

Lectures, homeworks, home reading, project, consultations

Assessment

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

Lecturer's references

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]
Ljupčo Todorovski
LUKŠIČ, Žiga, TANEVSKI, Jovan, DŽEROSKI, Sašo, TODOROVSKI, Ljupčo. Meta-model framework for surrogate-basedparameter estimation in dynamical systems. IEEE access. 2019, vol. 7, str. 181829 -181841. [COBISS-SI-ID 33102631]
BRENCE, Jure, TODOROVSKI, Ljupčo, DŽEROSKI, Sašo. Probabilistic grammars for equation discovery. Knowledge-based systems. [Print ed.]. 2021, vol. 224, str. 107077-1-107077-12. [COBISS-SI-ID 61709059]
GRAU LEGUIA, Marc, LEVNAJIĆ, Zoran, TODOROVSKI, Ljupčo, ŽENKO, Bernard. Reconstructing dynamical networks via feature ranking. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2019, vol. 29, no. 9, str. 093107. [COBISS-SI-ID 32629031]