Data mining

Course title

Data mining

Code

F18L3S150

Study programme

—

Organizer of the study programme (unit, institute, department)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First cycle

Academic year / semester

6 / Summer

Number of ECTS credits

Teacher

—

Prerequisites for enrolling in the course

(Веројатност и статистика или Бизнис статистика) или Бази на податоци

10.

Course objectives (competences)

. Introduction to methods for identifying valid, novel, useful, and understandable patterns in data. Data preprocessing Induction of predictive models from data: classification, regression, probability estimation. Discovery of clusters and association rules

11.

Course content

. Introduction. Data preprocessing. Performing data mining tasks: Classification, prediction, association analysis and clustering. Methods for analysis and comparison of knowledge discovering models. All algorithms will be applied to appropriately selected data sets.

12.

Learning methods

lectures, classroom exercises, laboratory exercises, project assignments, homework, development of a software package with data mining methods

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of the available time

30 + 45 + 0 + 30 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures – theoretical teaching	30 hours
15.2.	Exercises (laboratory, auditory), seminars, team work	45 hours

16.

Other forms of activities

16.1.	Project assignments	30 hours
16.2.	Individual assignments	0 hours
16.3.	Home study	75 hours

17.

Grading

17.1.	Tests	10 points
17.2.	Seminar paper / project (presentation: written and oral)	30 points
17.3.	Activity and participation	10 points
17.4.	Final exam	60 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.

Requirements for signature and taking the final exam

Completed activities 15, 16

20.

Language of instruction

Macedonian and English

21.

Method of monitoring teaching quality

internal evaluation and survey mechanism

22.

Literature