Subject

Data Visualisation

Course Title

Data Visualisation
Data visualization

Code

m23_w_022

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

9 / Winter

Number of ECTS credits

Teacher

Katarina Troyanets Dineva, Suzana Loshkova

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

The goal of the course is to introduce students to the theory and practical application of data visualization. Upon completion of the course, the student is expected to demonstrate knowledge of the concept of data visualization, to be able to select and implement visualization algorithms for different types of data programmatically and using visualization tools.

11.

Course content

Introduction. Basic concepts and terminology. Representation and structure of the dataset, data primitives, data structures. Visualization algorithms. Visualization of scalar data. Visualization of non-numerical data, multidimensional data, 3D techniques; dynamic techniques, distortion techniques, zooming and focusing; hybrid techniques. Interaction. Animation for visualization.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, practical classes (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

60 + 120 + 0 + 0 + 0 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	60 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	120 hours

16.

Other forms of activities

16.1.	Project assignments	0 hours
16.2.	Independent assignments	0 hours
16.3.	Home study	0 hours

17.

Assessment method

17.1.	Tests	40 points
17.2.	Seminar paper / project (presentation: written and oral)	0 points
17.3.	Activities and learning	0 points
17.4.	Final exam	0 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

completed activities

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. Colin Ware \| Information Visualization - Perception for Design \| Morgan Kaufman \| 2021 2. Kieran Healy \| Data Visualization \| Princeton University Press \| 2019 3. Claus O Wilke \| Fundamentals of Data Visualization \| O'Reilly \| 2019
22.2.	Additional literature No. Author Title Publisher Year