Klaida
  • Įvyko klaida įkeliant kanalų duomenys

Data visualization methods (INF3041)

Course code

Course group

Volume in ECTS credits

Course hours

INF 3041

C

4

116

Course type (compulsory or optional)

Compulsory

Course level (study cycle)

Bachelor

Semester the course is delivered

Spring

Study form (face-to-face or distant)

Face-to-face

Course title in Lithuanian

DUOMENŲ VIZUALIZAVIMO METODAI

Course title in English

DATA VISUALIZATION METHODS

Short course annotation in Lithuanian

Tikslas - supažindinti su duomenų vizualizavimo sritimi: pagrindinėmis sąvokomis,  vizualizavimo raida, pagrindiniais grafinio dizaino principais, duomenų tipais ir duomenų apdorojimo/paruošimo vizualizacijai metodikomis, grafikų tipais ir jų taikymo atvejais, daugiamačių duomenų vizualizavimo metodais, jų taikymais ir juos realizuojančiais įrankiais.

Short course annotation in English

This course aims to provide the student the theoretical and practical bases of data visualization methods and develop understanding in the field of data visualization: basic data visualization concepts and evolution of visualization, main graphic design principles, data types and data pre-processing/preparation techniques, plot types and application cases, multidimensional data visualization methods, application cases and visualization tools. Teaching methods are: lectures and practical works.

Prerequisites for entering the course

Probability theory and mathematical statistics

Course aim

Course aim is to provide knowledge on graphical design principles, data preprocessing techniques, data visualization methods and visualization tools.

Content (topics)

 1. Evolution of visualization
 2. Graphical design principles
 3. Data types
 4. Data preprocessing
 5. Types of plots
 6. Graphs and trees
 7. Direct visualization methods
 8. Linear projection methods
 9. Non-linear projection methods

Practical work (contents):

Laboratory assignments are prepared to teach students to generate visual representations taking into account graphical design principles, to preprocess data for visualization, to select appropriate visualization method, to create and interpret visual representations and to work with various software packages (Matlab, MS Excel, Orange) as data analysis and visualization tools.

Distribution of workload for students (contact and independent work hours)

Lectures – 30 hours, practical works – 30 hours, individual work – 56 hours.

Structure of cumulative score and value of its constituent parts

Laboratory assignments – 33%, written midterm examination – 17%, written final examination – 50%.

Recommended reference materials

No.

 

Publication year

Authors of publication and title

Publishing house

Number of copies in

University library

Self-study rooms

Other libraries 

Basic materials

1.

2008

Chen C., Hardle W., Unwin A. Handbook

of Data Visualization

Springer-Verlag

 

1

 

2.

2001

Tufte Edward R. The Visual Display of Quantitative Information

Graphics Press

 

1

 

3.

2001

Friendly M., Denis D.J.  Milestones in the history of thematic cartography, statistical

graphics, and data visualization

 

 

 

1

4.

2008

Dzemyda G., Kurasova O., Žilinskas J., Daugiamačių duomenų vizualizavimo metodai

Vilnius : Mokslo aidai

7

 

1

Course programme designed by

dr. Aušra Mackutė-Varoneckienė, Department of Applied Informatics, Faculty of Informatics

Additional information