Introduction to datascience

1.

Subject title

Introduction to datascience

Вовед во науката за податоци

2.

Code

F23L3W008

3.

Study program

Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Информатичка едукација, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Примена на информациски технологии, Софтверско инженерство и информациски системи, Software engineering and information systems, Стручни студии за програмирање, Стручни студии за програмирање, Примена на информациски технологии, Софтверско инженерство и информациски системи, Software engineering and information systems, Statistics and Data Analytics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Прв циклус

6.

Academic year / semester

3 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Димитар Трајанов проф. д-р Георгина Мирчева проф. д-р Игор Мишковски ворн. проф. д-р Милош Јовановиќ ворн. проф. д-р Мирослав Мирчев проф. д-р Слободан Калајџиски проф. д-р Весна Димитрова

9.

Prerequisites for enrollment

Бизнис статистика или Веројатност и статистика или Основи на теорија на информации или Математика 3

10.

Subject goals and competencies:

Introduction to the basics of data-driven science. Students will get to know the process and methodology when working with data, starting from the identification of problems, through data collection, and then their processing. Students will learn the basic techniques for data processing and identifying patterns in them, as well as ways of visualizing and interpreting the obtained results.

11.

Subject content:

(2) Introduction to data science as a fourth scientific paradigm (2) Designing experiments and identifying problems (2) Data collection and processing (2) Before data processing (2) Identification of patterns in data and visualization (2) Introduction to Machine Learning (2) Basic models for Machine Learning (2) Deep learning (2) Unsupervised learning (clustering, dimensionality reduction) (2) Introduction to Natural Language Processing

12.

Learning methods:

Предавања со користење на презентации, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа.

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

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

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

0 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

15 points

17.4.

Final exam

65 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.

Condition for signature and taking final exam

Реализирани актибвности 15.2 и 16.1

20.

Language of instruction

Македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature