Data processing

1.

Subject title

Data processing

Обработка на податоци

2.

Code

BI-I-02

3.

Study program

Cloud Computing, Data science in computer science and engineering, IT management, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Security, Cryptography and Coding, Software Engineering, Bioinformatics, Bioinformatics, Statistics and Data Analytics, Statistics and Data Analytics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

доц. д-р Илинка Иваноска проф. д-р Слободан Калајџиски

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

This course will primarily focus on developing the skills needed to support other courses in the program. While studying this course, students will develop a systematic understanding of the principles of computer science and data science, which are under the foundations of many interdisciplinary programs. Students will be offered broad knowledge in key areas of computer science relevant to modern research, including the ability to evaluate data and identify appropriate tools for their examination and manipulation. This course students will acquire the following competencies: - Knowledge of the key elements of advanced programming in scientific research - Knowledge of the key elements of modern script and analytical languages ??(such as but not limited to R and Python) o Manipulation of data o Basic procedures for data analysis o Creating graphs - Knowledge of approaches and methods for visualizing complex data using modern script languages. - collecting, extracting and manipulating large data sets using command line and scripting tools. - Designing, writing, anotating, testing and debugging of analytical code. - Designing, justifying and implementing a data processing flow, which includes more calculation tools in order to examine questions about research oriented outcome.

11.

Subject content:

The course will be built from four thematic areas: 1. Advanced System Skills for Different Interdisciplinary Studies (Linux) 2. Introduction to r 3. Introduction to Python 4. Pre -processing and cleaning data 5. Methods for visualizing data

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

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

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

15 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.

Condition for signature and taking final exam

реализирани активности 15

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature