Statistical modelling

Course title

Code

F18L3S163

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

10.

Course objectives (competences)

The students should learn how to make the statistical analysis of data, using the classical and Bayesian approach. They should set suitable statistical models and check and interpret the obtained results. The accent will be on the use of open source software (R, Python) for building models based on real data, using the presented theory. The course should prepare the students for data analysis in different research fields.

11.

Course content

Every topic will be illustrated with corresponding real world examples.
Introduction to data modelling. General methods for simulation of random variables. Correlation and linear regression., estimation of functional dependance of data. Parametric models of regression functions. Simple linear regression. Analysis of variance (ANOVA). Multiple regression. Logistic regression, binary and multiple logistic regression. Choice of classification model. Principle component analysis,log-linear models. Nonparametric estimation of regression functions and classification:nearest neighbour method, naive Bayes. Support vector machine. Comparison of the methods. Bayesian methods for parameter estimation. Models for collection of data for Bayesian modelling.

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lectures, 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 the available time

30 + 45 + 15 + 15 + 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	15 hours
16.2.	Individual assignments	15 hours
16.3.	Home study	75 hours

17.

Grading

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activity and participation	10 points
17.4.	Final exam	80 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

Activities 15.2 and 16.1 have been completed.

20.

Language of instruction

Macedonian and English

21.

Method of monitoring teaching quality

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature No. Author Title Publisher Year
22.2.	Additional literature No. Author Title Publisher Year