Subject

Data Processing

Course Title

Data Processing
Data processing

Code

Biological Ionization 02

Study Programme

Bioinformatics

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

Ilinka Ivanoska, Slobodan Kajaldjiski

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

This course will primarily focus on developing the skills needed to support the 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 foundational to many interdisciplinary programs. Students will be provided with broad knowledge in the key areas of computer science relevant to contemporary research, including the ability to evaluate data and identify appropriate tools for its examination and manipulation. With 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 scripting and analytical languages (such as, but not limited to, R and Python)
o data manipulation
Basic data analysis procedures
Creating charts
- Knowledge of approaches and methods for visualizing complex data using modern scripting languages.
Collecting, extracting, and manipulating large datasets using the command line and scripting tools.
- Designing, writing, annotating, testing and debugging analytical code.
- Designing, justifying and implementing a computational workflow for data processing that includes multiple computing tools for examining outcome-oriented research questions.

11.

Course content

The course will be built around four thematic areas:
Advanced System Skills for Various Interdisciplinary Studies (Linux)
2. Introduction to R
3. Introduction to Python
4. Data preprocessing and cleaning
5. Data visualization methods

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, invited guest lecturers, independent preparation and defense of a project assignment and seminar paper, learning in an electronic environment (forums, consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

60 + 0 + 45 + 45 + 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	45 hours
16.2.	Independent assignments	45 hours
16.3.	Home study	30 hours

17.

Assessment method

17.1.	Tests	15 points
17.2.	Seminar paper / 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.

Requirement for obtaining a signature and taking the final exam

Activities completed 15

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. Vince Buffalo \| Bioinformatics Data Skills \| O'Reilly Media \| 2015 2. Steven Haddock, Casey Dunn \| Practical Computing for Biologists \| Oxford University Press \| 2011 3.
22.2.	Additional literature No. Author Title Publisher Year