Subject

Introduction to Ecoinformatics

Course Title

Introduction to Ecoinformatics
Introduction to Ecoinformatics

Code

F23L2S084

Study Programme

—

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

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

4 / Summer

Number of ECTS credits

Teacher

Andrea Naumoski, Kosta Mitreski

Prerequisites for enrolling in the course

Introduction to Computer Science

10.

Objectives of the course programme (competences)

Ecoinformatics is the science of information (informatics) in ecology and environmental science. It integrates ecological and informatics sciences to define entities and natural processes with a language common to humans and computers. Ecoinformatics aims to facilitate environmental research and management by developing ways to access and integrate knowledge from different sources of environmental information, and by developing new algorithms that enable the combination of different environmental data to test ecological hypotheses.

11.

Course content

Lectures:
1. Introduction to Ecoinformatics.
2. Tools for analyzing data from the natural system.
3. Monitoring and acquisition of the data needed for Ecoinformatics.
4. Ecological Modeling - Dynamic Models
5. Ecological Modeling - Empirical Models
6. Tools used in modeling
7. Visualization of the data and the results obtained.
8. Visualization Tools
9. Software Tools for Machine Learning in Ecoinformatics.
10. Software tools for machine learning (1)
11. Software Tools for Machine Learning (2)
12. Examples of Ecoinformatics (1)

Practical Classes:
1. Examples of Applications of Ecoinformatics
2. Examples - Tools for analyzing data from the natural system.
3. Examples - Monitoring and acquisition of data needed for Ecoinformatics.
4. Examples - Ecological Modeling - Dynamic Models
5. Examples - Ecological Modeling - Empirical Models
6. Examples - Tools used in modeling
7. Examples
8. Examples - Visualization Tools
9. Examples—Software Tools for Machine Learning in Ecinformatics.
10. Machine Learning Tool Examples (1)
11. Machine Learning Toolkits (2)
12. Examples

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

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	10 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	10 points
17.4.	Final exam	30 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

Activities Implemented 15.2

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

22.2.

Additional literature

No. Author Title Publisher Year