Data warehouses and analytics

1.

Subject title

Data warehouses and analytics

Складови на податоци и аналитичка обработка

2.

Code

F23L3S157

3.

Study program

Компјутерско инженерство, Примена на информациски технологии, Софтверско инженерство и информациски системи, Интернет, мрежи и безбедност, Информатичка едукација, Software engineering and information systems, Примена на информациски технологии, Софтверско инженерство и информациски системи, Интернет, мрежи и безбедност, Software engineering and information systems, Компјутерски науки, Стручни студии за програмирање, Стручни студии за програмирање, Компјутерски науки, Компјутерско инженерство, Statistics and Data Analytics, Software Engineering,

4.

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

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Прв циклус

6.

Academic year / semester

3 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

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

9.

Prerequisites for enrollment

Освоени најмалку 100 ЕКТС

10.

Subject goals and competencies:

Introduction to organization and manipulation of data organized in data warehouses, as well as the basic operations in creating data warehouses. The students would be able to integrate operational data stores in specially designed models suitable for analytical workloads. This includes dimensional modeling of data warehouses and data marts, as well as data vaults. Likewise, it includes organization and manipulation of data in data warehouses and preparing analytical reports.

11.

Subject content:

Basic concepts in data warehouses; Architecture of data warehouses and Data flow; Modeling of data warehouses and data marts; Slowly changing dimensions; Organization of data in star and snowflake schemas; Data vault modeling; Hypercubes and multi-dimensional data stores; Online analytical processing (OLAP) technologies and extension of SQL standard for OLAP queries; Lineage between operational data stores and data warehouses; Automatic loads (incremental and full) in data warehouses, data cleansing and wrangling processes, aggregation, and Extract-Transform-Load (ETL). Basic concepts in distributed data warehouses and Big Data Analysis; Showcase and analysis of leading tools and technologies for creating, modeling and maintaining data warehouses.

12.

Learning methods:

предавања, аудиториски вежби, лабораториски вежби, проектни задачи, домашни задачи

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

10 points

17.4.

Final exam

70 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, 16

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