Subject

Numerical Methods for Data Science

Course Title

Numerical Methods for Data Science
Numerical Methods for Data Sciences

Code

m23_s_001

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

10 / Summer

Number of ECTS credits

Teacher

Vesna Dimitrievska Ristovska

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

1) Formulation of data-driven analytical models for optimal decision-making in various applications;
2) ability to analyze such models based on an understanding of their properties;
3) studying techniques for obtaining numerical solutions for such models through computer software;
4) Interpreting numerical solutions obtained in relation to optimal decisions.

11.

Course content

1. Linear models for optimization.
1.1. Introduction: decision optimization, analytical and operational research; formulations; graphical and software-based solutions.
1.2. Duality; economic interpretation; optimality conditions; sensitivity analysis; robustness.
1.3. Applications.

2. Discrete optimization models.
2.1. Formulations; graphical solution; linear relaxations; optimal gap.
2.2. Constrained methods; valid inequalities; applications.

3. Dynamic models for optimization.
3.1. Formulations; optimality equations; numerical solution; applications.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), independent preparation and defense of a project assignment and a seminar paper, learning in an electronic environment (forums, consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

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

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	60 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	0 hours

16.

Other forms of activities

16.1.	Project assignments	45 hours
16.2.	Independent assignments	45 hours
16.3.	Home study	30 hours

17.

Assessment method

17.1.	Tests	30 points
17.2.	Seminar paper / 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.

Requirement for obtaining a signature and taking the final exam

NULL

20.

Language of instruction

Macedonian

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. F.S. Hillier, G.J. Lieberman. \| Introduction to Operations Research. \| McGraw-Hill \| 2015 2. H.A. Taha. \| Operations Research: An Introduction \| Pearson / Prentice Hall \| 2007
22.2.	Additional literature No. Author Title Publisher Year