Advanced Data Science

1.

Subject title

Advanced Data Science

Напредни теми од наука базирана на податоци

2.

Code

m23_s_036

3.

Study program

Cloud Computing, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Inteligent Systems, Computer Science, Software Engineering, Cloud Computing, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering, Statistics and Data Analytics, Data science in computer science and engineering, IT management, Eco-informatics, Internet Technologies and cyber security, Software for embedded systems, IT management,

4.

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

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Ана Мадевска Богданова проф. д-р Димитар Трајанов проф. д-р Игор Мишковски ворн. проф. д-р Милош Јовановиќ проф. д-р Слободан Калајџиски проф. д-р Соња Гиевска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

This course is focused on how to answer business questions using advanced data science techniques effectively. By the end of the course, students will be able to recognize different types of questions (for example, descriptive, causal, or predictive questions), decide which methodological approaches are most appropriate for answering each type of question, be able to design and critically evaluate ML models as well as tailor the presentation of results to different audiences.

11.

Subject content:

From analytics to action Combining different types of data Selection and algorithms for solving the problem Algorithm design evaluations Algorithm Impact Assessments Real-time use cases Communication and persuasion through data Storytelling with data

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, практични вежби, тимска работа, пример случаи, поканети предавачи, самостојна изработка на проектна задача и семинарска работа и електронско учење.

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

45 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

50 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

40 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

50 points

17.3.

Activities and learning

10 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.

Condition for signature and taking final exam

реализирани активности

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