PA055 Visualizing Complex Data

Faculty of Informatics
Autumn 2020
Extent and Intensity
1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Matej Lexa, Ph.D. (lecturer)
Guaranteed by
doc. Ing. Matej Lexa, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Mon 12:00–13:50 B311
Prerequisites
Elementary programming skills and interest in R and Processing (scripting and programming languages)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 74 fields of study the course is directly associated with, display
Course objectives
Students will get aquainted with complex data in bioinformatics and selected other disciplines and their visualization, using examples in R and PROCESSING languages or scientific literature.
Learning outcomes
At the end of the course students will be able to:
explain the basic principles and goals of visualization
prepare data for visualization
evaluate existing visualizations
create their own static or interactive visualization
Syllabus
  • 1. Introduction to data visualization
  • 2. The R computing environment and its visualization tools
  • 3. The Processing computing environment and its visualization tools
  • 4. Visualization and data types in bioinformatics and system biology
  • 4. Data preprocessing (dimensionality estimates and reduction, PCA, clustering, similarity metrics, multidimensional scaling)
  • 6. Review of isualization techniques (plots, histograms, trees and other graphs, maps, hybrid visualization)
  • 7. Examples of visualization in bioinformatics, systems biology and other disciplines
Literature
  • Handbook of data visualization. Edited by Chun-houh Chen - Wolfgang Härdle - Antony Unwin. Berlin: Springer, 2008, xiii, 936. ISBN 9783540330370. info
  • SARKAR, Deepayan. Lattice : multivariate data visualization with R. New York: Springer, 2008, xvii, 265. ISBN 9780387759685. info
  • FRY, Ben. Visualizing data. Beijing: O'Reilly, 2008, xiii, 366. ISBN 9780596514556. info
Teaching methods
lectures, computer exercises, short student presentations
Assessment methods
short exercises, group project and a written exam
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2021, Autumn 2022.
  • Enrolment Statistics (Autumn 2020, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2020/PA055