Subject

Statistical Programming

Course Title

Statistical Programming
Statistical programming

Code

m23_s_054

Study Programme

Data statistics and analytics

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

Kire Trivodaliiev, Maria Mihkova

Prerequisites for enrolling in the course

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10.

Objectives of the course programme (competences)

The course includes advanced use of a statistical programming language of choice (R and/or Python) and aims to introduce students to the principles and applications of these languages, with a special focus on statistical programming in the chosen language.

11.

Course content

Basic commands in R and Python (arithmetic, logical, and vector operations; simulation of random variables)
Data structures and data handling. Plots in R (parcels, lines and dots, legends)
Functions and scripts (simple functions, for/while loops, if/else conditional statements)
Fast cycles and efficient programming (vector arithmetic, vectors versus functions, apply/mapply)
Computer-intensive techniques (simulation techniques, random tests, Monte Carlo integration, bootstrapping, Gibbs sampling)

12.

Learning methods

NULL

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

60 + 0 + 30 + 60 + 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	60 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	30 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	60 points
17.3.	Activities and learning	0 points
17.4.	Final exam	0 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

—

22.

Literature

22.1.	Required literature 1. Crawley, M. \| The R Book (2nd edition). \| Wiley. \| 2013 2. Thomas Mailund \| Functional Data Structures in R: Advanced Statistical Programming in R \| Apress \| 2017
22.2.	Additional literature No. Author Title Publisher Year