FI:PA055 Visualizing Complex Data - Course Information

PA055 Visualizing Complex Data

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. RNDr. Aleš Horák, 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 14:00–15:50 A219

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

• Applied Informatics (programme FI, B-AP)
• Applied Informatics (programme FI, N-AP)
• Information Technology Security (eng.) (programme FI, N-IN)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics (programme FI, B-AP)
• Bioinformatics (programme FI, N-AP)
• Economic Information Systems (programme ESF, B-SI)
• Information Systems (programme FI, N-IN)
• Informatics with another discipline (programme FI, B-BI)
• Informatics with another discipline (programme FI, B-EB)
• Informatics with another discipline (programme FI, B-FY)
• Informatics with another discipline (programme FI, B-GE)
• Informatics with another discipline (programme FI, B-GK)
• Informatics with another discipline (programme FI, B-CH)
• Informatics with another discipline (programme FI, B-IO)
• Informatics with another discipline (programme FI, B-MA)
• Informatics with another discipline (programme FI, B-TV)
• Informatics (eng.) (programme FI, D-IN4)
• Informatics (programme FI, B-IN)
• Informatics (programme FI, D-IN4)
• Informatics (programme FI, N-IN)
• Mathematical Informatics (programme FI, B-IN)
• Mathematics with Informatics (programme PřF, N-MA)
• Parallel and Distributed Systems (programme FI, B-IN)
• Parallel and Distributed Systems (programme FI, N-IN)
• Computer Graphics and Image Processing (programme FI, B-IN)
• Computer Graphics (programme FI, N-IN)
• Computer Networks and Communication (programme FI, B-IN)
• Computer Networks and Communication (programme FI, N-IN)
• Computer Systems and Technologies (eng.) (programme FI, D-IN4)
• Computer Systems and Technologies (programme FI, D-IN4)
• Computer Systems and Data Processing (programme FI, B-IN)
• Computer Systems (programme FI, N-IN)
• Embedded Systems (eng.) (programme FI, N-IN)
• Programmable Technical Structures (programme FI, B-IN)
• Embedded Systems (programme FI, N-IN)
• Service Science, Management and Engineering (eng.) (programme FI, N-AP)
• Service Science, Management and Engineering (programme FI, N-AP)
• Social Informatics (programme FI, B-AP)
• Theoretical Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Image Processing (programme FI, N-AP)

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.
At the end of the course they 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.

• Enrolment Statistics (Autumn 2017, recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2017/PA055