Subject

Introduction to Data Science

Course Title

Introduction to Data Science
Introduction to datascience

Code

F23L3W008

Study Programme

Примена на информациски технологии, Софтверско инженерство и информациски системи, Software engineering and information systems

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

5 / Winter

Number of ECTS credits

Teacher

Димитар Трајанов, Георгина Мирчева, Игор Мишковски, Милош Јовановиќ, Мирослав Мирчев, Слободан Калајџиски, Весна Димитрова

Prerequisites for enrolling in the course

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

10.

Objectives of the course programme (competences)

Introduction to the fundamentals of data-driven science. Students will be introduced to the process and methodology of working with data, starting with problem identification, followed by data collection, and then processing. Students will learn the basic techniques for data processing and pattern identification, as well as methods for visualizing and interpreting the results obtained.

11.

Course content

(2) Вовед во науката за податоци како четврта научна парадигма
(2) Designing experiments and identifying problems
(2) Collection and processing of data
(2) Пред процесирање на податоците
(2) Идентификација на шаблони во податоците и визуелизација
(2) Вовед во Машинско Учење
(2) Основни модели за Машинско Учење
(2) Длабоко учење
(2) Ненадгледувано учење (кластерирање, намалување на димензионалност)
(2) Вовед во обработка на природни јазици

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper.

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	0 points
17.2.	Seminar paper / 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.

Requirement for obtaining a signature and taking the final exam

Activities 15.2 and 16.1 have been completed.

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 1. Ethem Alpaydin \| Machine Learning \| MIT Press \| 2021 2. John D. Kelleher, Brendan Tierney \| Data Science \| MIT Press Essential Knowledge series \| 2018 3. Edward Raff \| Inside Deep Learning: Math, Algorithms, Models \| Manning \| 2022 4. François Chollet \| Deep Learning with Python, 2nd edition \| Manning \| 2021
22.2.	Additional literature No. Author Title Publisher Year