Numerical Methods for Data Sciences

1.

Subject title

Numerical Methods for Data Sciences

Нумерички методи за податочни науки

2.

Code

m23_s_001

3.

Study program

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

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 Весна Димитриевска Ристовска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

1) formulating analytical models based on optimal decision making in various applications; 2) Ability to analyze such models based on understanding their properties; 3) learning techniques for obtaining numerical solutions for such models through computer software; 4) Interpreting numerical solutions obtained in terms of optimal decisions.

11.

Subject content:

1. Linear models for SEO. 1.1. INTRODUCTION: SEO decision -making, analytical and operational research; formulations; Graphic and software-based solution. 1.2. Duality; economic interpretation; conditions of optimality; sensitivity analysis; robustness. 1.3. Applications. 2. Discrete SEO models. 2.1. Formulations; graphic solution; linear relaxations; Optimal gap. 2.2. Methods with restrictions; valid inequality; Applications. 3. Dynamic SEO models. 3.1. Formulations; Optimality equations; numerical solution; Applications.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

30 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

0 points

17.4.

Final exam

10 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

NULL

20.

Language of instruction

македонски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature