Subject

Big Data Concepts and Applications

Course Title

Big Data Concepts and Applications
Concepts and applications of big data

Code

m23_w_039

Study Programme

—

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

9 / Winter

Number of ECTS credits

Teacher

Eftim Zdravevski, Goran Velinov

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

The goal of the course is for students to become familiar with the phenomenon of big data—the reasons for its emergence and the ways it is generated—as well as the theoretical and practical concepts for modeling and analyzing data with large volume, velocity, and variety. An introduction to traditional data analysis systems and the challenges associated with big data will be provided. Typical problems, applications, and systems related to big data will be reviewed. From a theoretical and practical perspective, the ecosystem built around the Hadoop framework will be studied—its purpose, concepts, and the architecture of its elements, as well as the core components of the ecosystem.

11.

Course content

Generation of big data. Real-world examples of the three types of big data sources: people, organizations, and sensors.
Recognition and description of big data characteristics: volume, velocity, variability, variety, value, visualization, and validity. Their impact on data collection, monitoring, storage, analysis, and report generation.
Procedure for obtaining value from big data through a structured analysis process.
Challenges and mistakes in collecting and analyzing big data.
Description of the architectural components of systems used for scalable analysis of large data.
Data-driven decision-making strategies.
Horizontal and vertical partitioning of data.
Challenges with dimensional modeling.
Modules of the Hadoop framework: Common, YARN, HDFS, MapReduce.
Core components of the Hadoop ecosystem: HBase, Spark, Hive, Pig.
Tools for visualizing large data.

12.

Learning methods

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

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

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

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	30 hours

16.

Other forms of activities

16.1.	Project assignments	45 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	45 hours

17.

Assessment method

17.1.	Tests	30 points
17.2.	Seminar paper / 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.

Requirement for obtaining a signature and taking the final exam

completed activities 15.1 and 15.2

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

22.2.

Additional literature

No. Author Title Publisher Year