Concepts and applications of big data

1.

Subject title

Concepts and applications of big data

Концепти и примена на големи податоци

2.

Code

m23_w_039

3.

Study program

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

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 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

ворн. проф. д-р Ефтим Здравевски проф. д-р Горан Велинов

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The purpose of the subject is to get acquainted with the phenomenon of large data - the reasons for their occurrence and the ways of creating them, as well as the theoretical and practical concepts for modeling and analyzing data with large scale, speed and diversity. Introduction to traditional data analysis systems and major data challenges will be given. Typical problems, applications and systems associated with large data will be reviewed. The theoretical and practical aspick will be studied the ecosema built around the Hadoop frame - the purpose, concepts and architecture of the Hadoop elements and the basic components of the ecosystem.

11.

Subject content:

Generating large data. Realistic examples with the three types of large data sources: people, organizations and sensors. Recognizing and description of large data characteristics: volume, speed, variability, variety, value, visualization, valence. Their impact on collection, monitoring, storage, analysis and reporting reports. Procedure to obtain large data value through a structured analysis process. Challenges and errors in collecting and analyzing large data. Description of the architectural components of the systems used for scalable large data analysis. Data-guidance strategies for decision-making. Horizontal and vertical data partition. Challenges with dimensional modeling. Hadoop frame modules: Common, Yarn, HDFS, Mapreduce. Basic components of Hadoop Ecosystem: Hbase, Spark, Hive, Pig. Large data visualization tools.

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

30 + 30 + 30 + 45 + 45 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

30 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

45 hours

17.

Grading method

17.1.

Tests

30 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

20 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 15.1 и 15.2

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