Exploratory predictive analytics

1.

Subject title

Exploratory predictive analytics

Истражувачка и предиктивна аналитика

2.

Code

SDP-Z-4

3.

Study program

Cloud Computing, Data science in computer science and engineering, Bioinformatics, 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, Bioinformatics, Security, Cryptography and Coding, Software Engineering, Statistics and Data Analytics, Statistics and Data Analytics, IT management, IT management,

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

доц. д-р Бојан Илијоски доц. д-р Илинка Иваноска проф. д-р Соња Гиевска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

Leading companies such as: Google, Facebook, and Netflix use predictive analytics to develop their products and services. The focus of the subject are 1) aspects of the process of research analysis of data for and 2) making simple predictive models for detection, understanding and deeper insight into the information that can be extracted from the data. Students will get acquainted with techniques for understanding and summarizing data, combining multiple data sources, deciding how to discover the templates present in the data to simplify further analysis in the development of more complex statistical models. Part of the subject covers the practical application of learned techniques in conducting a research analysis of data and making a predictive model for initial knowledge insight present in the data.

11.

Subject content:

Preparation process for predictive analysis data: detection, structure, cleaning, enrichment, validation, publication. Steps in the research analysis. Dealing with missing data. Predictive analytics guided by data. Selection of appropriate machine learning techniques for an initial research study with data available. Practical application of machine learning for prediction. Dealing with dimensionality and optimization of predictive models. Developing a simple predictive model with real data and data sets for testing hypotheses, data templates and predictive analysis.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на софтверски пакети), самостојна изработка и одбрана на проектна задача и семинарска работа

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

0 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

0 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

NULL

20.

Language of instruction

Македонски – Англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature